diff --git a/examples/data/ts/M4DailyTest.csv b/examples/data/ts/M4DailyTest.csv
new file mode 100644
index 000000000..49d1acc56
--- /dev/null
+++ b/examples/data/ts/M4DailyTest.csv
@@ -0,0 +1,510082 @@
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diff --git a/examples/data/ts/M4MonthlyTest.csv b/examples/data/ts/M4MonthlyTest.csv
new file mode 100644
index 000000000..ac18cb48d
--- /dev/null
+++ b/examples/data/ts/M4MonthlyTest.csv
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diff --git a/examples/data/ts/M4QuarterlyTest.csv b/examples/data/ts/M4QuarterlyTest.csv
new file mode 100644
index 000000000..f0978df19
--- /dev/null
+++ b/examples/data/ts/M4QuarterlyTest.csv
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diff --git a/examples/data/ts/M4WeeklyTest.csv b/examples/data/ts/M4WeeklyTest.csv
new file mode 100644
index 000000000..8dfb00094
--- /dev/null
+++ b/examples/data/ts/M4WeeklyTest.csv
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diff --git a/examples/data/ts/M4Yearly.csv b/examples/data/ts/M4Yearly.csv
deleted file mode 100644
index ac487795d..000000000
--- a/examples/data/ts/M4Yearly.csv
+++ /dev/null
@@ -1,3693 +0,0 @@
-idx,value,label
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-1992-12-31 12:00:00,3033.697,Y30
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-1994-12-31 12:00:00,3171.19,Y30
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-1996-12-31 12:00:00,3310.781,Y30
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-1999-12-31 12:00:00,3694.991,Y30
-2000-12-31 12:00:00,3794.95,Y30
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-2002-12-31 12:00:00,3871.611,Y30
-2003-12-31 12:00:00,3896.205,Y30
-2004-12-31 12:00:00,3948.334,Y30
-2005-12-31 12:00:00,4026.801,Y30
-2006-12-31 12:00:00,4124.802,Y30
-2007-12-31 12:00:00,4427.045,Y30
-2008-12-31 12:00:00,4326.569,Y30
-2009-12-31 12:00:00,4352.551,Y30
-2010-12-31 12:00:00,4408.16,Y30
-2011-12-31 12:00:00,4450.216,Y30
-2012-12-31 12:00:00,4490.119,Y30
-2013-12-31 12:00:00,4552.869,Y30
-1989-12-31 12:00:00,2929.463,Y37
-1990-12-31 12:00:00,2991.189,Y37
-1991-12-31 12:00:00,3036.968,Y37
-1992-12-31 12:00:00,3049.555,Y37
-1993-12-31 12:00:00,3078.156,Y37
-1994-12-31 12:00:00,3063.687,Y37
-1995-12-31 12:00:00,3100.691,Y37
-1996-12-31 12:00:00,3150.507,Y37
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-2000-12-31 12:00:00,3340.854,Y37
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-2002-12-31 12:00:00,3375.198,Y37
-2003-12-31 12:00:00,3395.998,Y37
-2004-12-31 12:00:00,3446.932,Y37
-2005-12-31 12:00:00,3500.501,Y37
-2006-12-31 12:00:00,3583.382,Y37
-2007-12-31 12:00:00,3967.057,Y37
-2008-12-31 12:00:00,3751.41,Y37
-2009-12-31 12:00:00,3742.367,Y37
-2010-12-31 12:00:00,3776.237,Y37
-2011-12-31 12:00:00,3798.934,Y37
-2012-12-31 12:00:00,3798.104,Y37
-2013-12-31 12:00:00,3831.152,Y37
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-2050-12-31 12:00:00,411.0,Y54
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-2051-12-31 12:00:00,456.32921875,Y407
-2052-12-31 12:00:00,463.00040625,Y407
-2053-12-31 12:00:00,491.77128125,Y407
-2054-12-31 12:00:00,509.24078125,Y407
-2055-12-31 12:00:00,527.388,Y407
-2056-12-31 12:00:00,540.2270625,Y407
-2057-12-31 12:00:00,551.1556875,Y407
-2058-12-31 12:00:00,558.131625,Y407
-2059-12-31 12:00:00,575.0655,Y407
-2060-12-31 12:00:00,605.2570625,Y407
-2061-12-31 12:00:00,622.6404375,Y407
-2062-12-31 12:00:00,631.0258125,Y407
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-2071-12-31 12:00:00,832.6074375,Y407
-2072-12-31 12:00:00,858.3155,Y407
-2073-12-31 12:00:00,896.1098125,Y407
-2074-12-31 12:00:00,846.0199375,Y407
-2075-12-31 12:00:00,848.1105,Y407
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-2077-12-31 12:00:00,927.8350625,Y407
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-2084-12-31 12:00:00,1108.88975,Y407
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-2086-12-31 12:00:00,1178.554375,Y407
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-2088-12-31 12:00:00,1316.784125,Y407
-2089-12-31 12:00:00,1358.223875,Y407
-2090-12-31 12:00:00,1389.299125,Y407
-2091-12-31 12:00:00,1372.772125,Y407
-2092-12-31 12:00:00,1386.35375,Y407
-2093-12-31 12:00:00,1434.157375,Y407
-2094-12-31 12:00:00,1480.134,Y407
-2095-12-31 12:00:00,1544.870875,Y407
-2096-12-31 12:00:00,1620.56975,Y407
-2097-12-31 12:00:00,1738.406125,Y407
-2098-12-31 12:00:00,1813.437625,Y407
-2099-12-31 12:00:00,1886.983375,Y407
-2100-12-31 12:00:00,2015.1755,Y407
-2101-12-31 12:00:00,2078.52225,Y407
-2102-12-31 12:00:00,2115.2445,Y407
-2103-12-31 12:00:00,2139.74425,Y407
-2104-12-31 12:00:00,2232.69275,Y407
-2105-12-31 12:00:00,2303.09425,Y407
-2106-12-31 12:00:00,2376.3425,Y407
-2107-12-31 12:00:00,2427.12675,Y407
-2108-12-31 12:00:00,2413.1145,Y407
-2109-12-31 12:00:00,2307.0085,Y407
-2110-12-31 12:00:00,2279.85575,Y407
-2111-12-31 12:00:00,2304.46475,Y407
-2112-12-31 12:00:00,2384.291,Y407
-2113-12-31 12:00:00,2483.68225,Y407
-2114-12-31 12:00:00,2570.68,Y407
-1992-12-31 12:00:00,321.346,Y751
-1993-12-31 12:00:00,313.927,Y751
-1994-12-31 12:00:00,692.835,Y751
-1995-12-31 12:00:00,1488.087,Y751
-1996-12-31 12:00:00,1640.411,Y751
-1997-12-31 12:00:00,1688.279,Y751
-1998-12-31 12:00:00,1569.191,Y751
-1999-12-31 12:00:00,1580.622,Y751
-2000-12-31 12:00:00,1702.501,Y751
-2001-12-31 12:00:00,1790.053,Y751
-2002-12-31 12:00:00,1890.783,Y751
-2003-12-31 12:00:00,2427.493,Y751
-2004-12-31 12:00:00,2803.017,Y751
-2005-12-31 12:00:00,2152.022,Y751
-2006-12-31 12:00:00,2240.348,Y751
-2007-12-31 12:00:00,2322.506,Y751
-2008-12-31 12:00:00,2264.306,Y751
-2009-12-31 12:00:00,2163.428,Y751
-2010-12-31 12:00:00,2065.215,Y751
-2011-12-31 12:00:00,1970.274,Y751
-2012-12-31 12:00:00,1911.188,Y751
-2013-12-31 12:00:00,1879.024,Y751
-2014-12-31 12:00:00,1919.91,Y751
-2015-12-31 12:00:00,1963.036,Y751
-1970-12-31 12:00:00,2487.4,Y839
-1971-12-31 12:00:00,2590.0,Y839
-1972-12-31 12:00:00,2630.0,Y839
-1973-12-31 12:00:00,2620.0,Y839
-1974-12-31 12:00:00,2650.0,Y839
-1975-12-31 12:00:00,2690.0,Y839
-1976-12-31 12:00:00,2700.0,Y839
-1977-12-31 12:00:00,2690.0,Y839
-1978-12-31 12:00:00,2730.0,Y839
-1979-12-31 12:00:00,2740.0,Y839
-1980-12-31 12:00:00,2785.6,Y839
-1981-12-31 12:00:00,2868.4,Y839
-1982-12-31 12:00:00,2989.3,Y839
-1983-12-31 12:00:00,3055.2,Y839
-1984-12-31 12:00:00,3011.8,Y839
-1985-12-31 12:00:00,2935.0,Y839
-1986-12-31 12:00:00,2919.9,Y839
-1987-12-31 12:00:00,2823.0,Y839
-1988-12-31 12:00:00,2741.8,Y839
-1989-12-31 12:00:00,2684.3,Y839
-1990-12-31 12:00:00,2657.8,Y839
-1991-12-31 12:00:00,2659.2,Y839
-1992-12-31 12:00:00,2658.6,Y839
-1993-12-31 12:00:00,2658.7,Y839
-1994-12-31 12:00:00,2672.9,Y839
-1995-12-31 12:00:00,2685.6,Y839
-1996-12-31 12:00:00,2684.1,Y839
-1997-12-31 12:00:00,2694.0,Y839
-1998-12-31 12:00:00,2696.7,Y839
-1999-12-31 12:00:00,2697.1,Y839
-2000-12-31 12:00:00,2726.7,Y839
-2001-12-31 12:00:00,2703.1,Y839
-2002-12-31 12:00:00,2705.4,Y839
-2003-12-31 12:00:00,2699.3,Y839
-2004-12-31 12:00:00,2681.0,Y839
-2005-12-31 12:00:00,2688.8,Y839
-2006-12-31 12:00:00,2697.7,Y839
-2007-12-31 12:00:00,2707.4,Y839
-2008-12-31 12:00:00,2714.3,Y839
-2009-12-31 12:00:00,2713.4,Y839
-2010-12-31 12:00:00,2755.4,Y839
-2011-12-31 12:00:00,2737.4,Y839
-2012-12-31 12:00:00,2727.4,Y839
-2013-12-31 12:00:00,2734.2,Y839
-2014-12-31 12:00:00,2753.1,Y839
-2015-12-31 12:00:00,2775.5,Y839
-1990-12-31 12:00:00,4230.0,Y1067
-1991-12-31 12:00:00,4350.0,Y1067
-1992-12-31 12:00:00,4530.0,Y1067
-1993-12-31 12:00:00,4700.0,Y1067
-1994-12-31 12:00:00,4730.0,Y1067
-1995-12-31 12:00:00,4740.0,Y1067
-1996-12-31 12:00:00,4770.0,Y1067
-1997-12-31 12:00:00,4770.0,Y1067
-1998-12-31 12:00:00,4870.0,Y1067
-1999-12-31 12:00:00,4930.0,Y1067
-2000-12-31 12:00:00,4840.0,Y1067
-2001-12-31 12:00:00,5000.0,Y1067
-2002-12-31 12:00:00,4810.0,Y1067
-2003-12-31 12:00:00,4850.0,Y1067
-2004-12-31 12:00:00,4850.0,Y1067
-2005-12-31 12:00:00,4870.0,Y1067
-2006-12-31 12:00:00,4870.0,Y1067
-2007-12-31 12:00:00,4790.0,Y1067
-2008-12-31 12:00:00,4910.0,Y1067
-2009-12-31 12:00:00,4980.0,Y1067
-2010-12-31 12:00:00,5040.0,Y1067
-2011-12-31 12:00:00,4970.0,Y1067
-2012-12-31 12:00:00,4860.0,Y1067
-2013-12-31 12:00:00,4910.0,Y1067
-2014-12-31 12:00:00,5000.0,Y1067
-2015-12-31 12:00:00,5000.0,Y1067
-1990-12-31 12:00:00,510.0,Y1516
-1991-12-31 12:00:00,750.0,Y1516
-1992-12-31 12:00:00,670.0,Y1516
-1993-12-31 12:00:00,730.0,Y1516
-1994-12-31 12:00:00,730.0,Y1516
-1995-12-31 12:00:00,690.0,Y1516
-1996-12-31 12:00:00,740.0,Y1516
-1997-12-31 12:00:00,750.0,Y1516
-1998-12-31 12:00:00,770.0,Y1516
-1999-12-31 12:00:00,810.0,Y1516
-2000-12-31 12:00:00,830.0,Y1516
-2001-12-31 12:00:00,890.0,Y1516
-2002-12-31 12:00:00,990.0,Y1516
-2003-12-31 12:00:00,1090.0,Y1516
-2004-12-31 12:00:00,1200.0,Y1516
-2005-12-31 12:00:00,1280.0,Y1516
-2006-12-31 12:00:00,1550.0,Y1516
-2007-12-31 12:00:00,1190.0,Y1516
-2008-12-31 12:00:00,1170.0,Y1516
-2009-12-31 12:00:00,1240.0,Y1516
-2010-12-31 12:00:00,1560.0,Y1516
-2011-12-31 12:00:00,1680.0,Y1516
-2012-12-31 12:00:00,1600.0,Y1516
-2013-12-31 12:00:00,1460.0,Y1516
-2014-12-31 12:00:00,1680.0,Y1516
-2015-12-31 12:00:00,1750.0,Y1516
-2016-12-31 12:00:00,1760.0,Y1516
-2063-12-31 12:00:00,1100.0,Y1720
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-2065-12-31 12:00:00,1490.0,Y1720
-2066-12-31 12:00:00,1620.0,Y1720
-2067-12-31 12:00:00,1670.0,Y1720
-2068-12-31 12:00:00,2000.0,Y1720
-2069-12-31 12:00:00,2420.0,Y1720
-2070-12-31 12:00:00,2090.0,Y1720
-2071-12-31 12:00:00,1960.0,Y1720
-2072-12-31 12:00:00,2120.0,Y1720
-2073-12-31 12:00:00,2540.0,Y1720
-2074-12-31 12:00:00,2760.0,Y1720
-2075-12-31 12:00:00,2780.0,Y1720
-2076-12-31 12:00:00,3250.0,Y1720
-2077-12-31 12:00:00,3530.0,Y1720
-2078-12-31 12:00:00,4630.0,Y1720
-2079-12-31 12:00:00,5050.0,Y1720
-2080-12-31 12:00:00,5200.0,Y1720
-2081-12-31 12:00:00,5040.0,Y1720
-2082-12-31 12:00:00,5740.0,Y1720
-2083-12-31 12:00:00,5710.0,Y1720
-2084-12-31 12:00:00,5940.0,Y1720
-2085-12-31 12:00:00,6260.0,Y1720
-2086-12-31 12:00:00,6070.0,Y1720
-2087-12-31 12:00:00,6320.0,Y1720
-2088-12-31 12:00:00,6950.0,Y1720
-2089-12-31 12:00:00,7330.0,Y1720
-2090-12-31 12:00:00,8480.0,Y1720
-2091-12-31 12:00:00,8150.0,Y1720
-2092-12-31 12:00:00,7740.0,Y1720
-2093-12-31 12:00:00,8830.0,Y1720
-2094-12-31 12:00:00,8650.0,Y1720
-2095-12-31 12:00:00,8970.0,Y1720
-2096-12-31 12:00:00,8620.0,Y1720
-2097-12-31 12:00:00,9050.0,Y1720
-2063-12-31 12:00:00,1114.0,Y2006
-2064-12-31 12:00:00,1184.0,Y2006
-2065-12-31 12:00:00,1263.0,Y2006
-2066-12-31 12:00:00,1375.0,Y2006
-2067-12-31 12:00:00,1477.0,Y2006
-2068-12-31 12:00:00,1601.0,Y2006
-2069-12-31 12:00:00,1746.0,Y2006
-2070-12-31 12:00:00,1870.0,Y2006
-2071-12-31 12:00:00,1947.0,Y2006
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-2073-12-31 12:00:00,2367.0,Y2006
-2074-12-31 12:00:00,2593.0,Y2006
-2075-12-31 12:00:00,2730.0,Y2006
-2076-12-31 12:00:00,3211.0,Y2006
-2077-12-31 12:00:00,3496.0,Y2006
-2078-12-31 12:00:00,3943.0,Y2006
-2079-12-31 12:00:00,4413.0,Y2006
-2080-12-31 12:00:00,4917.0,Y2006
-2081-12-31 12:00:00,5345.0,Y2006
-2082-12-31 12:00:00,5703.0,Y2006
-2083-12-31 12:00:00,6125.0,Y2006
-2084-12-31 12:00:00,6778.0,Y2006
-2085-12-31 12:00:00,7208.0,Y2006
-2086-12-31 12:00:00,7934.0,Y2006
-2087-12-31 12:00:00,8842.0,Y2006
-2088-12-31 12:00:00,9819.0,Y2006
-2089-12-31 12:00:00,10535.0,Y2006
-2090-12-31 12:00:00,10901.0,Y2006
-2091-12-31 12:00:00,10703.0,Y2006
-2092-12-31 12:00:00,11179.0,Y2006
-2093-12-31 12:00:00,11662.0,Y2006
-2094-12-31 12:00:00,12244.0,Y2006
-2095-12-31 12:00:00,12776.0,Y2006
-2096-12-31 12:00:00,13249.0,Y2006
-2097-12-31 12:00:00,13998.0,Y2006
-1969-12-31 12:00:00,184.0,Y2315
-1970-12-31 12:00:00,180.0,Y2315
-1971-12-31 12:00:00,177.0,Y2315
-1972-12-31 12:00:00,190.0,Y2315
-1973-12-31 12:00:00,196.0,Y2315
-1974-12-31 12:00:00,192.0,Y2315
-1975-12-31 12:00:00,217.0,Y2315
-1976-12-31 12:00:00,248.0,Y2315
-1977-12-31 12:00:00,271.0,Y2315
-1978-12-31 12:00:00,313.0,Y2315
-1979-12-31 12:00:00,315.0,Y2315
-1980-12-31 12:00:00,364.0,Y2315
-1981-12-31 12:00:00,453.0,Y2315
-1982-12-31 12:00:00,460.0,Y2315
-1983-12-31 12:00:00,540.0,Y2315
-1984-12-31 12:00:00,588.0,Y2315
-1985-12-31 12:00:00,634.0,Y2315
-1986-12-31 12:00:00,651.0,Y2315
-1987-12-31 12:00:00,706.0,Y2315
-1988-12-31 12:00:00,721.0,Y2315
-1989-12-31 12:00:00,969.0,Y2315
-1990-12-31 12:00:00,1114.0,Y2315
-1991-12-31 12:00:00,1200.0,Y2315
-1992-12-31 12:00:00,1219.0,Y2315
-1993-12-31 12:00:00,1422.0,Y2315
-1994-12-31 12:00:00,1514.0,Y2315
-1995-12-31 12:00:00,1756.0,Y2315
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-2013-12-31 12:00:00,3273.3,Y4649
-2014-12-31 12:00:00,3459.8,Y4649
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-2010-12-31 12:00:00,1890.0,Y4685
-2011-12-31 12:00:00,1930.0,Y4685
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-2013-12-31 12:00:00,2060.0,Y4685
-2014-12-31 12:00:00,2160.0,Y4685
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-2013-12-31 12:00:00,6158.6,Y4811
-2014-12-31 12:00:00,6620.6,Y4811
-2015-12-31 12:00:00,6797.8,Y4811
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-2010-12-31 12:00:00,6305.76,Y5264
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-2012-12-31 12:00:00,7698.53,Y5264
-2013-12-31 12:00:00,7454.2,Y5264
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-2008-12-31 12:00:00,8952.47,Y5375
-2009-12-31 12:00:00,9226.03,Y5375
-2010-12-31 12:00:00,9695.99,Y5375
-2011-12-31 12:00:00,10425.59,Y5375
-2012-12-31 12:00:00,10657.34,Y5375
-2013-12-31 12:00:00,10625.61,Y5375
-2014-12-31 12:00:00,10830.47,Y5375
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-1996-12-31 12:00:00,11522.0828708,Y13427
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-2008-12-31 12:00:00,12779.4067682,Y13427
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-2008-12-31 12:00:00,1860.0,Y14960
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-2010-12-31 12:00:00,1670.0,Y14960
-2011-12-31 12:00:00,1640.0,Y14960
-2012-12-31 12:00:00,1660.0,Y14960
-2013-12-31 12:00:00,1690.0,Y14960
-2014-12-31 12:00:00,1710.0,Y14960
-2015-12-31 12:00:00,1720.0,Y14960
-1994-12-31 12:00:00,84.385,Y15158
-1995-12-31 12:00:00,110.568,Y15158
-1996-12-31 12:00:00,129.527,Y15158
-1997-12-31 12:00:00,181.872,Y15158
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-2014-12-31 12:00:00,5292.429,Y16267
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-2013-12-31 12:00:00,5510.0,Y16578
-2014-12-31 12:00:00,5840.0,Y16578
-2015-12-31 12:00:00,5960.0,Y16578
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diff --git a/examples/data/ts/ts_long.csv b/examples/data/ts/ts_long.csv
deleted file mode 100644
index 713c2fe6e..000000000
--- a/examples/data/ts/ts_long.csv
+++ /dev/null
@@ -1,35041 +0,0 @@
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-2018-09-28 17:00:00,5695.0,traffic_volume
-2018-09-28 18:00:00,5104.0,traffic_volume
-2018-09-28 19:00:00,4286.0,traffic_volume
-2018-09-28 20:00:00,3538.0,traffic_volume
-2018-09-28 21:00:00,3058.0,traffic_volume
-2018-09-28 22:00:00,2713.0,traffic_volume
-2018-09-28 23:00:00,3544.0,traffic_volume
-2018-09-29 00:00:00,1733.0,traffic_volume
-2018-09-29 01:00:00,867.0,traffic_volume
-2018-09-29 02:00:00,524.0,traffic_volume
-2018-09-29 03:00:00,359.0,traffic_volume
-2018-09-29 04:00:00,425.0,traffic_volume
-2018-09-29 05:00:00,743.0,traffic_volume
-2018-09-29 06:00:00,1359.0,traffic_volume
-2018-09-29 07:00:00,2036.0,traffic_volume
-2018-09-29 08:00:00,3073.0,traffic_volume
-2018-09-29 09:00:00,3725.0,traffic_volume
-2018-09-29 10:00:00,4059.0,traffic_volume
-2018-09-29 11:00:00,4378.0,traffic_volume
-2018-09-29 12:00:00,4657.0,traffic_volume
-2018-09-29 13:00:00,4553.0,traffic_volume
-2018-09-29 14:00:00,4618.0,traffic_volume
-2018-09-29 15:00:00,4564.0,traffic_volume
-2018-09-29 16:00:00,4735.0,traffic_volume
-2018-09-29 17:00:00,4878.0,traffic_volume
-2018-09-29 18:00:00,4569.0,traffic_volume
-2018-09-29 19:00:00,3818.0,traffic_volume
-2018-09-29 20:00:00,3338.0,traffic_volume
-2018-09-29 21:00:00,2950.0,traffic_volume
-2018-09-29 22:00:00,2607.0,traffic_volume
-2018-09-29 23:00:00,3856.0,traffic_volume
-2018-09-30 00:00:00,1826.0,traffic_volume
-2018-09-30 01:00:00,920.0,traffic_volume
-2018-09-30 02:00:00,696.0,traffic_volume
-2018-09-30 03:00:00,364.0,traffic_volume
-2018-09-30 04:00:00,335.0,traffic_volume
-2018-09-30 05:00:00,444.0,traffic_volume
-2018-09-30 06:00:00,802.0,traffic_volume
-2018-09-30 07:00:00,1175.0,traffic_volume
-2018-09-30 08:00:00,2002.0,traffic_volume
-2018-09-30 09:00:00,2882.0,traffic_volume
-2018-09-30 10:00:00,3701.0,traffic_volume
-2018-09-30 11:00:00,4049.0,traffic_volume
-2018-09-30 12:00:00,4429.0,traffic_volume
-2018-09-30 13:00:00,4547.0,traffic_volume
-2018-09-30 14:00:00,4380.0,traffic_volume
-2018-09-30 15:00:00,4302.0,traffic_volume
-2018-09-30 16:00:00,4283.0,traffic_volume
-2018-09-30 17:00:00,4132.0,traffic_volume
-2018-09-30 18:00:00,3947.0,traffic_volume
-2018-09-30 19:00:00,3543.0,traffic_volume
-2018-09-30 20:00:00,2781.0,traffic_volume
-2018-09-30 21:00:00,2159.0,traffic_volume
-2018-09-30 22:00:00,1450.0,traffic_volume
-2018-09-30 23:00:00,954.0,traffic_volume
diff --git a/examples/data/ts/ts_sea_level.csv b/examples/data/ts/ts_sea_level.csv
deleted file mode 100644
index 90b60fc91..000000000
--- a/examples/data/ts/ts_sea_level.csv
+++ /dev/null
@@ -1,3785 +0,0 @@
-Date,Level,Neighboring level
-2009-01-01,0.5961000000000001,0.5951000000000001
-2009-01-02,0.5958,0.5886
-2009-01-03,0.5955,0.5871000000000001
-2009-01-04,0.5961000000000001,0.5833
-2009-01-05,0.5964,0.5814
-2009-01-06,0.5949,0.5787
-2009-01-07,0.5982000000000001,0.5769000000000001
-2009-01-08,0.6003000000000001,0.5784
-2009-01-09,0.6047,0.5829
-2009-01-10,0.6072000000000001,0.5844
-2009-01-11,0.6137,0.5856
-2009-01-12,0.614,0.5857
-2009-01-13,0.6143000000000001,0.5881000000000001
-2009-01-14,0.6124,0.5878
-2009-01-15,0.6123000000000001,0.5887
-2009-01-16,0.6115,0.5908
-2009-01-17,0.6131,0.5903
-2009-01-18,0.6112000000000001,0.5912000000000001
-2009-01-19,0.6075,0.5894
-2009-01-20,0.6052000000000001,0.5904
-2009-01-21,0.6013000000000001,0.5919
-2009-01-22,0.6005,0.5928
-2009-01-23,0.5968,0.5926
-2009-01-24,0.5952000000000001,0.5929
-2009-01-25,0.5899,0.5926
-2009-01-26,0.5905,0.5932000000000001
-2009-01-27,0.5920000000000001,0.5942000000000001
-2009-01-28,0.5972000000000001,0.596
-2009-01-29,0.6036,0.5964
-2009-01-30,0.6102000000000001,0.5996
-2009-01-31,0.618,0.6007
-2009-02-01,0.6269,0.6046
-2009-02-02,0.6357,0.608
-2009-02-03,0.6446000000000001,0.6119
-2009-02-04,0.6513,0.619
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-2009-02-06,0.6713,0.6278
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-2009-02-09,0.6947,0.6499
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-2009-02-11,0.7007,0.6617000000000001
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-2009-02-13,0.7078,0.6745
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-2009-02-15,0.7063,0.6900000000000001
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-2009-02-17,0.7018,0.6962
-2009-02-18,0.7037,0.6995
-2009-02-19,0.7020000000000001,0.7040000000000001
-2009-02-20,0.7024,0.7076
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-2009-02-22,0.7017,0.7100000000000001
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-2009-02-24,0.6961,0.7131000000000001
-2009-02-25,0.6936,0.7143
-2009-02-26,0.6873,0.7141000000000001
-2009-02-27,0.6843,0.7142000000000001
-2009-02-28,0.6821,0.7137
-2009-03-01,0.6799000000000001,0.7117
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-2009-03-08,0.6744,0.6961
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-2009-03-10,0.6764,0.6943
-2009-03-11,0.6802,0.6924
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-2009-03-13,0.6843,0.6904
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-2009-03-15,0.6880000000000001,0.6862
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-2009-04-10,0.8342,0.8025
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-2009-04-12,0.8476,0.8114
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-2009-04-15,0.8588,0.8260000000000001
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-2009-04-17,0.8618,0.8327
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-2009-04-19,0.862,0.8378
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-2009-04-22,0.8502000000000001,0.8454
-2009-04-23,0.8456,0.8455
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-2009-04-25,0.8288000000000001,0.8461000000000001
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-2009-04-27,0.8109000000000001,0.8438
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-2009-05-03,0.7689,0.8094
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-2009-05-05,0.7616,0.7983
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-2009-05-08,0.7602,0.7799
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-2009-05-11,0.7658,0.7711
-2009-05-12,0.769,0.7646000000000001
-2009-05-13,0.773,0.7644000000000001
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-2009-05-17,0.8004,0.7628
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-2009-05-21,0.8292,0.779
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-2009-05-23,0.8418,0.7897000000000001
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-2009-05-25,0.8492000000000001,0.8022
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-2009-05-27,0.8533000000000001,0.8151
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-2009-05-29,0.8606,0.8221
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-2009-05-31,0.8678,0.8338
-2009-06-01,0.8729,0.8381000000000001
-2009-06-02,0.8773000000000001,0.8434
-2009-06-03,0.879,0.8489
-2009-06-04,0.8829,0.8539
-2009-06-05,0.8836,0.8574
-2009-06-06,0.8887,0.8624
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-2009-06-08,0.8997,0.8738
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-2009-06-10,0.911,0.8821
-2009-06-11,0.9152,0.8856
-2009-06-12,0.9169,0.8906000000000001
-2009-06-13,0.9223,0.8977
-2009-06-14,0.9238,0.903
-2009-06-15,0.9256,0.9079999999999999
-2009-06-16,0.925,0.9123
-2009-06-17,0.9224,0.9171
-2009-06-18,0.9215,0.92
-2009-06-19,0.9179,0.9237
-2009-06-20,0.9173,0.9251
-2009-06-21,0.912,0.9252
-2009-06-22,0.9095,0.9229
-2009-06-23,0.9037,0.9211
-2009-06-24,0.902,0.9159
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-2009-06-28,0.9009,0.9009999999999999
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-2009-07-11,0.8445,0.8828
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-2009-07-13,0.8325,0.8711000000000001
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-2009-07-15,0.8210000000000001,0.8608
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-2009-07-21,0.8130000000000001,0.8426
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-2009-07-25,0.8088000000000001,0.8413
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-2009-07-28,0.8019000000000001,0.8375
-2009-07-29,0.7956000000000001,0.8334
-2009-07-30,0.792,0.8294
-2009-07-31,0.7912,0.8268000000000001
-2009-08-01,0.7847000000000001,0.8231
-2009-08-02,0.7813,0.8220000000000001
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-2019-04-28,0.8183,0.8206
-2019-04-29,0.8215,0.8210000000000001
-2019-04-30,0.8242,0.8226
-2019-05-01,0.8286,0.8230000000000001
-2019-05-02,0.8321000000000001,0.8277
-2019-05-03,0.8338,0.8317
-2019-05-04,0.8344,0.8350000000000001
-2019-05-05,0.8341000000000001,0.8372
-2019-05-06,0.8342,0.8389000000000001
-2019-05-07,0.8332,0.8426
-2019-05-08,0.8296,0.8467
-2019-05-09,0.8296,0.8472000000000001
-2019-05-10,0.8282,0.8466
-2019-05-11,0.8271000000000001,0.8471000000000001
-2019-05-12,0.8262,0.8462000000000001
diff --git a/examples/ts_forecasting/with_decomposition.py b/examples/ts_forecasting/with_decomposition.py
new file mode 100644
index 000000000..33a8ab68d
--- /dev/null
+++ b/examples/ts_forecasting/with_decomposition.py
@@ -0,0 +1,78 @@
+import random
+
+import numpy as np
+import pandas as pd
+from fedot.core.composer.metrics import smape
+from fedot.core.data.data import InputData
+from fedot.core.data.data_split import train_test_data_setup
+from fedot.core.pipelines.pipeline_builder import PipelineBuilder
+from fedot.core.repository.dataset_types import DataTypesEnum
+from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
+from matplotlib import pyplot as plt
+
+from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels
+
+datasets = {
+    'm4_yearly': f'../data/ts/M4YearlyTest.csv',
+    'm4_weekly': f'../data/ts/M4WeeklyTest.csv',
+    'm4_daily': f'../data/ts/M4DailyTest.csv',
+    'm4_monthly': f'../data/ts/M4MonthlyTest.csv',
+    'm4_quarterly': f'../data/ts/M4QuarterlyTest.csv'}
+
+
+def get_ts_data(dataset='m4_monthly', horizon: int = 30, m4_id=None):
+    time_series = pd.read_csv(datasets[dataset])
+
+    task = Task(TaskTypesEnum.ts_forecasting,
+                TsForecastingParams(forecast_length=horizon))
+    if not m4_id:
+        label = random.choice(np.unique(time_series['label']))
+    else:
+        label = m4_id
+    print(label)
+    time_series = time_series[time_series['label'] == label]
+
+    if dataset not in ['australia']:
+        idx = pd.to_datetime(time_series['idx'].values)
+    else:
+        # non datetime indexes
+        idx = time_series['idx'].values
+
+    time_series = time_series['value'].values
+    train_input = InputData(idx=idx,
+                            features=time_series,
+                            target=time_series,
+                            task=task,
+                            data_type=DataTypesEnum.ts)
+    train_data, test_data = train_test_data_setup(train_input)
+    return train_data, test_data, label
+
+
+if __name__ == '__main__':
+
+    forecast_length = 13
+
+    train_data, test_data, label = get_ts_data('m4_monthly', forecast_length)
+
+    with IndustrialModels():
+        pipeline = PipelineBuilder().add_node('data_driven_basis_for_forecasting',
+                                              params={'window_size': int(len(train_data.features) * 0.35)}
+                                              ).build()
+        pipeline.fit(train_data)
+        ssa_predict = np.ravel(pipeline.predict(test_data).predict)
+
+    baseline = PipelineBuilder().add_node('ar').build()
+    baseline.fit(train_data)
+    no_ssa = np.ravel(baseline.predict(test_data).predict)
+
+    plt.title(label)
+    plt.plot(train_data.idx, test_data.features, label='features')
+    plt.plot(test_data.idx, test_data.target, label='target')
+    plt.plot(test_data.idx, ssa_predict, label='predicted ssa')
+    plt.plot(test_data.idx, no_ssa, label='predicted baseline')
+    plt.grid()
+    plt.legend()
+    plt.show()
+
+    print(f"SSA smape: {smape(test_data.target, ssa_predict)}")
+    print(f"no SSA smape: {smape(test_data.target, no_ssa)}")
diff --git a/fedot_ind/api/main.py b/fedot_ind/api/main.py
index 905408970..ba3e6e54d 100644
--- a/fedot_ind/api/main.py
+++ b/fedot_ind/api/main.py
@@ -93,6 +93,9 @@ def __init_solver(self):
             #     solver = TaskEnum[self.config_dict['task']].value['nn']
             else:
                 solver = TaskEnum[self.config_dict['task']].value['default']
+        elif self.config_dict['task'] == 'ts_forecasting':
+            if self.config_dict['strategy'] == 'decomposition':
+                solver = TaskEnum[self.config_dict['task']].value['fedot_preset']
 
         else:
             solver = TaskEnum[self.config_dict['task']].value[0]
diff --git a/fedot_ind/core/architecture/experiment/TimeSeriesForecasingWithDecomposition.py b/fedot_ind/core/architecture/experiment/TimeSeriesForecasingWithDecomposition.py
new file mode 100644
index 000000000..e5757411d
--- /dev/null
+++ b/fedot_ind/core/architecture/experiment/TimeSeriesForecasingWithDecomposition.py
@@ -0,0 +1,148 @@
+import logging
+from typing import List, Union
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+from fedot.api.main import Fedot
+from fedot.core.data.data import InputData
+from fedot.core.operations.operation_parameters import OperationParameters
+from fedot.core.pipelines.node import PipelineNode
+from fedot.core.pipelines.pipeline import Pipeline
+from fedot.core.pipelines.pipeline_builder import PipelineBuilder
+from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
+from fedot.core.repository.dataset_types import DataTypesEnum
+from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum
+from fedot.core.repository.tasks import Task, TaskTypesEnum
+from golem.core.tuning.simultaneous import SimultaneousTuner
+
+from fedot_ind.api.utils.path_lib import default_path_to_save_results
+from fedot_ind.core.metrics.evaluation import PerformanceAnalyzer
+from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels
+
+np.random.seed(0)
+
+
+class TimeSeriesForecastingWithDecompositionPreset:
+    """.
+
+    """
+
+    def __init__(self, params: Optional[OperationParameters] = None):
+        self.test_data_preprocessed = None
+
+        self.model_params = params.get('model_params')
+        self.dataset_name = params.get('dataset')
+        self.output_dir = params.get('output_dir', default_path_to_save_results())
+
+        self.logger = logging.getLogger('TimeSeriesForecastingWithDecomposition')
+
+        self.prediction_label = None
+        self.predictor = None
+        self.y_train = None
+        self.train_features = None
+        self.test_features = None
+        self.input_test_data = None
+
+        self.logger.info('initialised')
+
+    # TODO: put some datatype
+    # TODO: add multidata option
+    def _init_input_data(self, X, y):
+        input_data = InputData(idx=np.arange(len(X)),
+                               features=X.values,
+                               target=y.values,
+                               task=Task(TaskTypesEnum.ts_forecasting), data_type=DataTypesEnum.table)
+
+        # Multidata option
+
+        # train_data = InputData(idx=np.arange(len(train_data[0])),
+        #                        features=np.array(train_data[0].values.tolist()),
+        #                        target=train_data[1].reshape(-1, 1),
+        #                        task=Task(TaskTypesEnum.classification), data_type=DataTypesEnum.image)
+
+        return input_data
+
+    def _build_pipeline(self):
+        pipeline_builder = PipelineBuilder().add_node('data_driven_basis_for_forecasting').add_node(
+            'series_reconstruction').build()
+        return pipeline_builder.build()
+
+    def _tune_pipeline(self, pipeline: Pipeline, train_data: InputData):
+        pipeline_tuner = TunerBuilder(train_data.task) \
+            .with_tuner(SimultaneousTuner) \
+            .with_metric(ClassificationMetricsEnum.f1) \
+            .with_iterations(30) \
+            .build(train_data)
+        pipeline = pipeline_tuner.tune(pipeline)
+        return pipeline
+
+    def fit(self, train_ts_frame,
+            train_target: np.ndarray = None,
+            **kwargs) -> object:
+
+        with IndustrialModels():
+            self.train_data = self._init_input_data(train_ts_frame, train_target)
+            self.prerpocessing_pipeline = self._build_pipeline()
+            self.prerpocessing_pipeline = self._tune_pipeline(self.prerpocessing_pipeline,
+                                                              self.train_data)
+            self.prerpocessing_pipeline.fit(self.train_data)
+
+            rf_node = self.prerpocessing_pipeline.nodes[0]
+            self.prerpocessing_pipeline.update_node(rf_node, PipelineNode('cat_features'))
+            rf_node.nodes_from = []
+            rf_node.unfit()
+            self.prerpocessing_pipeline.fit(self.train_data)
+
+            train_data_preprocessed = self.prerpocessing_pipeline.root_node.predict(self.train_data)
+            train_data_preprocessed.predict = np.squeeze(train_data_preprocessed.predict)
+
+            train_data_preprocessed = InputData(idx=train_data_preprocessed.idx,
+                                                features=train_data_preprocessed.predict,
+                                                target=train_data_preprocessed.target,
+                                                data_type=train_data_preprocessed.data_type,
+                                                task=train_data_preprocessed.task)
+
+        metric = 'roc_auc' if train_data_preprocessed.num_classes == 2 else 'f1'
+        self.model_params.update({'metric': metric})
+        self.predictor = Fedot(**self.model_params)
+
+        self.predictor.fit(train_data_preprocessed)
+
+        return self.predictor
+
+    def predict(self, test_features, test_target) -> dict:
+        if self.test_data_preprocessed is None:
+            test_data = self._init_input_data(test_features, test_target)
+            test_data_preprocessed = self.prerpocessing_pipeline.root_node.predict(test_data)
+            test_data_preprocessed.predict = np.squeeze(test_data_preprocessed.predict)
+            self.test_data_preprocessed = InputData(idx=test_data_preprocessed.idx,
+                                                    features=test_data_preprocessed.predict,
+                                                    target=test_data_preprocessed.target,
+                                                    data_type=test_data_preprocessed.data_type,
+                                                    task=test_data_preprocessed.task)
+
+        self.prediction_label = self.predictor.predict(self.test_data_preprocessed)
+        return self.prediction_label
+
+    def predict_proba(self, test_features, test_target) -> dict:
+        if self.test_data_preprocessed is None:
+            test_data = self._init_input_data(test_features, test_target)
+            test_data_preprocessed = self.prerpocessing_pipeline.root_node.predict(test_data)
+            self.test_data_preprocessed.predict = np.squeeze(test_data_preprocessed.predict)
+
+        self.prediction_proba = self.predictor.predict_proba(self.test_data_preprocessed)
+        return self.prediction_proba
+
+    def get_metrics(self, target: Union[np.ndarray, pd.Series], metric_names: Union[str, List[str]]):
+        analyzer = PerformanceAnalyzer()
+        return analyzer.calculate_metrics(target=target,
+                                          predicted_labels=self.prediction_label,
+                                          predicted_probs=self.prediction_proba,
+                                          target_metrics=metric_names)
+
+    def save_prediction(self, predicted_data: np.ndarray, kind: str):
+        self.saver.save(predicted_data, kind)
+
+    def save_metrics(self, metrics: dict):
+        self.saver.save(metrics, 'metrics')
diff --git a/fedot_ind/core/architecture/pipelines/classification.py b/fedot_ind/core/architecture/pipelines/classification.py
index 0d34c42de..9dbf523af 100644
--- a/fedot_ind/core/architecture/pipelines/classification.py
+++ b/fedot_ind/core/architecture/pipelines/classification.py
@@ -2,7 +2,7 @@
 from pymonad.either import Right
 from fedot_ind.core.architecture.pipelines.abstract_pipeline import AbstractPipelines
 from fedot_ind.core.architecture.preprocessing.DatasetLoader import DataLoader
-from fedot_ind.core.operation.transformation.basis.data_driven import DataDrivenBasisImplementation
+from fedot_ind.core.operation.implementation.basis.data_driven import DataDrivenBasisImplementation
 from functools import partial
 
 
diff --git a/fedot_ind/core/architecture/settings/pipeline_factory.py b/fedot_ind/core/architecture/settings/pipeline_factory.py
index 057200994..2e563a6fa 100644
--- a/fedot_ind/core/architecture/settings/pipeline_factory.py
+++ b/fedot_ind/core/architecture/settings/pipeline_factory.py
@@ -7,9 +7,10 @@
 from fedot_ind.core.models.recurrence.RecurrenceExtractor import RecurrenceExtractor
 from fedot_ind.core.models.signal.SignalExtractor import SignalExtractor
 from fedot_ind.core.models.topological.TopologicalExtractor import TopologicalExtractor
+from fedot_ind.core.operation.implementation.basis.fourier import FourierBasisImplementation
+from fedot_ind.core.operation.implementation.basis.wavelet import WaveletBasisImplementation
 from fedot_ind.core.operation.transformation.basis.data_driven import DataDrivenBasisImplementation
-from fedot_ind.core.operation.transformation.basis.fourier import FourierBasisImplementation
-from fedot_ind.core.operation.transformation.basis.wavelet import WaveletBasisImplementation
+
 
 
 class BasisTransformations(Enum):
diff --git a/fedot_ind/core/architecture/settings/task_factory.py b/fedot_ind/core/architecture/settings/task_factory.py
index 34ea3b2d1..b72a82f5f 100644
--- a/fedot_ind/core/architecture/settings/task_factory.py
+++ b/fedot_ind/core/architecture/settings/task_factory.py
@@ -6,6 +6,7 @@
 from fedot_ind.core.architecture.experiment.TimeSeriesClassifierNN import TimeSeriesClassifierNN
 from fedot_ind.core.architecture.experiment.TimeSeriesClassifierPreset import TimeSeriesClassifierPreset
 from fedot_ind.core.architecture.experiment.TimeSeriesRegression import TimeSeriesRegression
+from fedot_ind.core.architecture.experiment import TimeSeriesForecasingWithDecomposition
 from fedot_ind.core.ensemble.rank_ensembler import RankEnsemble
 
 
@@ -24,3 +25,5 @@ class TaskEnum(Enum):
     image_classification = (CVExperimenter,)
     object_detection = (CVExperimenter,)
     semantic_segmentation = (CVExperimenter,)
+
+
diff --git a/fedot_ind/core/models/signal/SignalExtractor.py b/fedot_ind/core/models/signal/SignalExtractor.py
index d91885da9..f5a4672b4 100644
--- a/fedot_ind/core/models/signal/SignalExtractor.py
+++ b/fedot_ind/core/models/signal/SignalExtractor.py
@@ -8,7 +8,7 @@
 from fedot_ind.core.metrics.metrics_implementation import *
 from fedot_ind.core.models.WindowedFeaturesExtractor import WindowedFeatureExtractor
 from fedot_ind.core.models.quantile.quantile_extractor import QuantileExtractor
-from fedot_ind.core.operation.transformation.basis.wavelet import WaveletBasisImplementation
+from fedot_ind.core.operation.implementation.basis.wavelet import WaveletBasisImplementation
 
 
 class SignalExtractor(WindowedFeatureExtractor):
diff --git a/fedot_ind/core/operation/IndustrialCachableOperation.py b/fedot_ind/core/operation/IndustrialCachableOperation.py
index 33b27d476..0160705a3 100644
--- a/fedot_ind/core/operation/IndustrialCachableOperation.py
+++ b/fedot_ind/core/operation/IndustrialCachableOperation.py
@@ -64,8 +64,5 @@ def transform(self, input_data: InputData, use_cache: bool = False) -> OutputDat
             predict = self._convert_to_output(input_data, predict, data_type=self.data_type)
             return predict
 
-    def _transform(self, input_data) -> np.array:
-        """
-            Method for feature generation for all series
-        """
+    def _transform(self, input_data):
         pass
diff --git a/fedot_ind/core/operation/decomposition/SpectrumDecomposition.py b/fedot_ind/core/operation/decomposition/SpectrumDecomposition.py
index db66f0961..8aae33ab3 100644
--- a/fedot_ind/core/operation/decomposition/SpectrumDecomposition.py
+++ b/fedot_ind/core/operation/decomposition/SpectrumDecomposition.py
@@ -1,11 +1,15 @@
 # -*- coding: utf-8 -*-
-from typing import Union
+from typing import Optional
 
 import numpy as np
 import pandas as pd
+import tensorly as tl
+from pymonad.list import ListMonad
+from tensorly.decomposition import parafac
 
-from fedot_ind.core.operation.transformation.data.hankel import HankelMatrix
-from fedot_ind.core.operation.transformation.regularization.spectrum import singular_value_hard_threshold
+from fedot_ind.core.operation.decomposition.matrix_decomposition.fast_svd import RSVDDecomposition
+from fedot_ind.core.operation.transformation.regularization.spectrum import reconstruct_basis, \
+    singular_value_hard_threshold
 
 supported_types = (pd.Series, np.ndarray, list)
 
@@ -15,148 +19,52 @@ class SpectrumDecomposer:
     recorded at equal intervals.
 
     Args:
-        time_series: The time series to decompose.
-        window_length: The length of the window to use. Defaults to None.
-        save_memory: Whether to save memory by not storing the elementary matrices. Defaults to True.
+        data: hankel matrix of time series
+        ts_length: The length of the time series
+        threshold: number of components.
 
     """
 
-    def __init__(self,
-                 time_series: Union[pd.DataFrame, pd.Series, np.ndarray, list],
-                 window_length: int = None,
-                 save_memory: bool = True):
-        self.__time_series = time_series
-        self.__convert_ts_to_array()
-        self.__window_length = window_length
-        if self.__window_length is None:
-            self.__window_length = round(self.__time_series.size * 0.35)
-        self.__save_memory = save_memory
-        self.__set_dimensions()
-        self.__check_windows_length()
-        self.__trajectory_matrix = self.__get_trajectory_matrix()
-
-    def __check_windows_length(self):
-        if not 2 <= self.__window_length <= self.__ts_length / 2:
-            raise ValueError("The window length must be in the interval [2, N/2].")
-
-    def __convert_ts_to_array(self):
-        if type(self.__time_series) == pd.DataFrame:
-            self.__time_series = self.__time_series.values
-        elif type(self.__time_series) == list:
-            self.__time_series = np.array(self.__time_series)
-        else:
-            self.__time_series = self.__time_series
-
-    def __set_dimensions(self):
-        self.__ts_length = len(self.__time_series)
-        self.__subseq_length = self.__ts_length - self.__window_length + 1
-
-    def __get_trajectory_matrix(self):
-        # return np.array(
-        #     [self.__time_series[i:self.__window_length + i] for i in range(0, self.__subseq_length)]).T
-        hankel_transform = HankelMatrix(time_series=self.__time_series, window_size=self.window_length)
-        if len(self.__time_series.shape) > 2:
-            return hankel_transform.trajectory_matrix
-        else:
-            return hankel_transform.trajectory_matrix
-
-    @property
-    def window_length(self):
-        return self.__window_length
-
-    @property
-    def sub_seq_length(self):
-        return self.__subseq_length
-
-    @window_length.setter
-    def window_length(self, window_length):
-        self.__window_length = window_length
-
-    @property
-    def trajectory_matrix(self):
-        return self.__trajectory_matrix
-
-    @property
-    def ts_length(self):
-        return self.__ts_length
-
-    @trajectory_matrix.setter
-    def trajectory_matrix(self, trajectory_matrix: np.ndarray):
-        self.__trajectory_matrix = trajectory_matrix
-
-    def decompose(self, rank_hyper: int = None):
-        # Embed the time series in a trajectory matrix
-        U, Sigma, VT = np.linalg.svd(self.__trajectory_matrix)
-
-        if rank_hyper is None:
-            sing_values = singular_value_hard_threshold(singular_values=Sigma)
-            rank = len(sing_values)
-
-        else:
-            rank = rank_hyper
-
-        # Decompose the trajectory matrix
-        TS_comps = np.zeros((self.__ts_length, rank))
-
-        if not self.__save_memory:
-            # Construct and save all the elementary matrices
-            X_elem = np.array([Sigma[i] * np.outer(U[:, i], VT[i, :]) for i in range(rank)])
-
-            # Diagonally average the elementary matrices, store them as columns in array.
-            for i in range(rank):
-                X_rev = X_elem[i, ::-1]
-                TS_comps[:, i] = [X_rev.diagonal(j).mean() for j in range(-X_rev.shape[0] + 1, X_rev.shape[1])]
-
-            V = VT.T
-        else:
-            # Reconstruct the elementary matrices without storing them
-            for i in range(rank):
-                X_elem = Sigma[i] * np.outer(U[:, i], VT[i, :])
-                X_rev = X_elem[::-1]
-                TS_comps[:, i] = [X_rev.diagonal(j).mean() for j in range(-X_rev.shape[0] + 1, X_rev.shape[1])]
-
-            X_elem = "Re-run with save_mem=False to retain the elementary matrices."
-
-            # The V array may also be very large under these circumstances, so we won't keep it.
-            V = "Re-run with save_mem=False to retain the V matrix."
-
-        return TS_comps, Sigma, rank, X_elem, V
-
-    @staticmethod
-    def components_to_df(TS_comps: np.ndarray, rank: int, n: int = 0) -> pd.DataFrame:
-        """Converts all the time series components in a single Pandas DataFrame object.
-
-        Args:
-            TS_comps: The time series components.
-            rank: The rank of the time series.
-            n: ...
-
-        Returns:
-            df: dataframe with all the time series components.
-        """
-        if n > 0:
-            n = min(n, rank)
-        else:
-            n = rank
-
-        # Create list of columns - call them F0, F1, F2, ...
-        cols = ["F{}".format(i) for i in range(n)]
-        df = pd.DataFrame(TS_comps.T).T.iloc[:, :rank]
-        df.columns = cols
-        return df
-
-    def reconstruct(self, indices: Union[int, list, tuple, np.ndarray]) -> pd.Series:
-        """Reconstructs the time series from its elementary components, using the given indices. Returns a Pandas Series
-        object with the reconstructed time series.
-
-        Args:
-            indices: An integer, list of integers or slice(n,m) object, representing the elementary
-            components to sum.
-
-        Returns:
-            The reconstructed time series.
-        """
-        if isinstance(indices, int): indices = [indices]
-
-        ts_vals = self.TS_comps[:, indices].sum(axis=1)
-        return pd.Series(ts_vals)
+    def __init__(self, data, ts_length, threshold: Optional[int] = None):
+        if type(data) == list:
+            self.rank = round(data[0].shape[0] / 10)
+            self.beta = data[0].shape[0] / data[0].shape[1]
+        self.ts_length = ts_length
+        self.svd_estimator = RSVDDecomposition()
+        self.low_rank_approximation = True
+        self.thr = threshold
+
+    def svd(self, x):
+        return ListMonad(self.svd_estimator.rsvd(tensor=x,
+                                                 approximation=self.low_rank_approximation,
+                                                 regularized_rank=self.thr))
+
+    def threshold(self, x):
+        return ListMonad([x[0],
+                          x[1][:self.thr],
+                          x[2]])
+
+    def multi_threshold(self, x):
+        return ListMonad([x[1],
+                          list(map(lambda x: singular_value_hard_threshold(
+                              singular_values=x,
+                              beta=self.beta,
+                              threshold=None), x[0])),
+                          x[2].T]) if self.thr is None else ListMonad([x[1][
+                                                                       :,
+                                                                       :self.thr],
+                                                                       x[0][
+                                                                       :,
+                                                                       :self.thr],
+                                                                       x[2][
+                                                                       :,
+                                                                       :self.thr].T])
+
+    def data_driven_basis(self, x):
+        return ListMonad(reconstruct_basis(x[0],
+                                           x[1],
+                                           x[2],
+                                           ts_length=self.ts_length))
+
+    def tensor_decomposition(self, x):
+        return ListMonad(parafac(tl.tensor(x), rank=self.rank).factors)
diff --git a/fedot_ind/core/operation/decomposition/matrix_decomposition/fast_svd.py b/fedot_ind/core/operation/decomposition/matrix_decomposition/fast_svd.py
index 82146d8e0..ce7548dbc 100644
--- a/fedot_ind/core/operation/decomposition/matrix_decomposition/fast_svd.py
+++ b/fedot_ind/core/operation/decomposition/matrix_decomposition/fast_svd.py
@@ -19,6 +19,8 @@ def _init_random_params(self, tensor):
         self.random_projection = np.random.randn(tensor.shape[1], projection_rank)
 
     def _compute_matrix_approximation(self, Ut, block, tensor, rank):
+        if rank < 3:
+            rank = 3
         Ut_ = Ut[:, :rank]
         tensor_approx = block @ Ut_
         reconstr_m = tensor_approx @ tensor_approx.T @ tensor
diff --git a/fedot_ind/core/operation/transformation/basis/__init__.py b/fedot_ind/core/operation/implementation/__init__.py
similarity index 100%
rename from fedot_ind/core/operation/transformation/basis/__init__.py
rename to fedot_ind/core/operation/implementation/__init__.py
diff --git a/fedot_ind/core/operation/implementation/basis/__init__.py b/fedot_ind/core/operation/implementation/basis/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/fedot_ind/core/operation/transformation/basis/abstract_basis.py b/fedot_ind/core/operation/implementation/basis/abstract_basis.py
similarity index 100%
rename from fedot_ind/core/operation/transformation/basis/abstract_basis.py
rename to fedot_ind/core/operation/implementation/basis/abstract_basis.py
diff --git a/fedot_ind/core/operation/implementation/basis/data_driven.py b/fedot_ind/core/operation/implementation/basis/data_driven.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/fedot_ind/core/operation/transformation/basis/fourier.py b/fedot_ind/core/operation/implementation/basis/fourier.py
similarity index 96%
rename from fedot_ind/core/operation/transformation/basis/fourier.py
rename to fedot_ind/core/operation/implementation/basis/fourier.py
index 4687b7b48..359d04a35 100644
--- a/fedot_ind/core/operation/transformation/basis/fourier.py
+++ b/fedot_ind/core/operation/implementation/basis/fourier.py
@@ -1,7 +1,7 @@
 from typing import Optional
 import numpy as np
 from fedot.core.operations.operation_parameters import OperationParameters
-from fedot_ind.core.operation.transformation.basis.abstract_basis import BasisDecompositionImplementation
+from fedot_ind.core.operation.implementation.basis.abstract_basis import BasisDecompositionImplementation
 
 
 class FourierBasisImplementation(BasisDecompositionImplementation):
diff --git a/fedot_ind/core/operation/transformation/basis/wavelet.py b/fedot_ind/core/operation/implementation/basis/wavelet.py
similarity index 97%
rename from fedot_ind/core/operation/transformation/basis/wavelet.py
rename to fedot_ind/core/operation/implementation/basis/wavelet.py
index b9d14cf1c..0d73a1d6c 100644
--- a/fedot_ind/core/operation/transformation/basis/wavelet.py
+++ b/fedot_ind/core/operation/implementation/basis/wavelet.py
@@ -4,7 +4,7 @@
 from fedot.core.operations.operation_parameters import OperationParameters
 from pymonad.either import Either
 from pymonad.list import ListMonad
-from fedot_ind.core.operation.transformation.basis.abstract_basis import BasisDecompositionImplementation
+from fedot_ind.core.operation.implementation.basis.abstract_basis import BasisDecompositionImplementation
 
 
 class WaveletBasisImplementation(BasisDecompositionImplementation):
diff --git a/fedot_ind/core/operation/interfaces/industrial_decomposition_for_forecasting_strategy.py b/fedot_ind/core/operation/interfaces/industrial_decomposition_for_forecasting_strategy.py
new file mode 100644
index 000000000..9b6981f10
--- /dev/null
+++ b/fedot_ind/core/operation/interfaces/industrial_decomposition_for_forecasting_strategy.py
@@ -0,0 +1,39 @@
+from typing import Optional
+
+from fedot.core.operations.evaluation.time_series import FedotTsForecastingStrategy
+from fedot.core.operations.operation_parameters import OperationParameters
+
+from fedot_ind.core.operation.ts_forecasting.data_driven_ts import DataDrivenForForecastingBasisImplementation
+from fedot_ind.core.repository.IndustrialOperationParameters import IndustrialOperationParameters
+
+
+class IndustrialDecompositionForForecastingStrategy(FedotTsForecastingStrategy):
+    """
+    Args:
+        operation_type: ``str`` of the operation defined in operation or data operation repositories
+
+            .. details:: possible operations:
+
+                - ``data_driven_basic``-> DataDrivenBasisImplementation,
+                - ``topological_features``-> TopologicalExtractor,
+
+
+        params: hyperparameters to fit the operation with
+
+    """
+
+    __operations_by_types = {
+        'data_driven_basis_for_forecasting': DataDrivenForForecastingBasisImplementation,
+    }
+
+    def __init__(self, operation_type: str, params: Optional[OperationParameters] = None):
+        self.operation_impl = self._convert_to_operation(operation_type)
+        params = IndustrialOperationParameters().from_params(operation_type, params) if params \
+            else IndustrialOperationParameters().from_operation_type(operation_type)
+        super().__init__(operation_type, params)
+
+    def _convert_to_operation(self, operation_type: str):
+        if operation_type in self.__operations_by_types.keys():
+            return self.__operations_by_types[operation_type]
+        else:
+            raise ValueError(f'Impossible to obtain custom preprocessing strategy for {operation_type}')
diff --git a/fedot_ind/core/operation/interfaces/industrial_preprocessing_strategy.py b/fedot_ind/core/operation/interfaces/industrial_preprocessing_strategy.py
index 30318e686..31ae5ed39 100644
--- a/fedot_ind/core/operation/interfaces/industrial_preprocessing_strategy.py
+++ b/fedot_ind/core/operation/interfaces/industrial_preprocessing_strategy.py
@@ -8,9 +8,11 @@
 from fedot_ind.core.models.quantile.quantile_extractor import QuantileExtractor
 from fedot_ind.core.models.topological.TopologicalExtractor import TopologicalExtractor
 from fedot_ind.core.operation.dummy.dummy_operation import DummyOperation
+
+from fedot_ind.core.operation.implementation.basis.fourier import FourierBasisImplementation
+from fedot_ind.core.operation.implementation.basis.wavelet import WaveletBasisImplementation
 from fedot_ind.core.operation.transformation.basis.data_driven import DataDrivenBasisImplementation
-from fedot_ind.core.operation.transformation.basis.fourier import FourierBasisImplementation
-from fedot_ind.core.operation.transformation.basis.wavelet import WaveletBasisImplementation
+
 from fedot_ind.core.repository.IndustrialOperationParameters import IndustrialOperationParameters
 
 
diff --git a/fedot_ind/core/operation/transformation/WindowSelection.py b/fedot_ind/core/operation/transformation/WindowSelection.py
index fb27225b9..1568c59ee 100644
--- a/fedot_ind/core/operation/transformation/WindowSelection.py
+++ b/fedot_ind/core/operation/transformation/WindowSelection.py
@@ -59,10 +59,16 @@ def __init__(self,
         self.length_ts = len(time_series)
 
         if self.window_max is None:
-            self.window_max = int(len(self.time_series) / 3)
+            if len(time_series) >= 100:
+                self.window_max = int(len(self.time_series) / 3)
+            else:
+                self.window_max = int(len(self.time_series) / 2)
 
         if self.window_min is None:
-            self.window_min = int(len(self.time_series) / 10)
+            if len(time_series) >= 100:
+                self.window_min = int(len(self.time_series) / 10)
+            else:
+                self.window_min = int(len(self.time_series) / 2) - 1
 
     def autocorrelation(self) -> Tuple[int, list]:
         """Main function for the highest_autocorrelation (AC) to find an appropriate window size.
@@ -302,6 +308,8 @@ def get_window_size(self) -> Tuple[int, list]:
             raise ValueError('Hyperparameters error! self.window_max is equal or smaller to self.window_min')
         else:
             window_size_selected, list_score = self.dict_methods[self.wss_algorithm]()
+            if window_size_selected < self.window_min:
+                return self.window_min, list_score
         return window_size_selected, list_score
 
 
diff --git a/fedot_ind/core/operation/transformation/basis/data_driven.py b/fedot_ind/core/operation/transformation/basis/data_driven.py
index f2e532e2f..b65273280 100644
--- a/fedot_ind/core/operation/transformation/basis/data_driven.py
+++ b/fedot_ind/core/operation/transformation/basis/data_driven.py
@@ -1,26 +1,19 @@
-import math
-from multiprocessing import Pool
-from typing import Optional, Tuple, TypeVar
-from scipy import stats
+from typing import Optional, TypeVar
+
 import numpy as np
 import pandas as pd
-import tensorly as tl
+from fedot.core.data.data import InputData
 from fedot.core.operations.operation_parameters import OperationParameters
 from joblib import Parallel, delayed
 from pymonad.either import Either
 from pymonad.list import ListMonad
+from scipy import stats
 from scipy.spatial.distance import cdist
-from tensorly.decomposition import parafac
 from tqdm import tqdm
 
-from fedot_ind.core.architecture.preprocessing import InputData
-from fedot.core.data.data import InputData
-from fedot_ind.core.operation.decomposition.matrix_decomposition.fast_svd import RSVDDecomposition
-
-from fedot_ind.core.operation.transformation.basis.abstract_basis import BasisDecompositionImplementation
+from fedot_ind.core.operation.decomposition.SpectrumDecomposition import SpectrumDecomposer
+from fedot_ind.core.operation.implementation.basis.abstract_basis import BasisDecompositionImplementation
 from fedot_ind.core.operation.transformation.data.hankel import HankelMatrix
-from fedot_ind.core.operation.transformation.regularization.spectrum import reconstruct_basis, \
-    singular_value_hard_threshold
 
 class_type = TypeVar("T", bound="DataDrivenBasis")
 
@@ -43,8 +36,7 @@ def __init__(self, params: Optional[OperationParameters] = None):
         self.SV_threshold = None
         # self.sv_selector = params.get('sv_selector')
         self.sv_selector = 'median'
-        self.svd_estimator = RSVDDecomposition()
-        self.low_rank_approximation = True
+
         self.logging_params.update({'WS': self.window_size,
                                     'SV_selector': self.sv_selector,
                                     })
@@ -72,7 +64,7 @@ def _transform(self, input_data: InputData) -> np.array:
         """Method for transforming all samples
 
         """
-        if type(input_data) is InputData:
+        if isinstance(input_data, InputData):
             features = np.array(ListMonad(*input_data.features.tolist()).value)
         else:
             features = np.array(ListMonad(*input_data.values.tolist()).value)
@@ -97,6 +89,15 @@ def _transform(self, input_data: InputData) -> np.array:
         # predict = self._clean_predict(np.array(v))
         return predict
 
+    def _transform_one_sample(self, series: np.array, svd_flag: bool = False):
+        trajectory_transformer = HankelMatrix(time_series=series, window_size=self.window_size)
+        data = trajectory_transformer.trajectory_matrix
+        self.window_size = trajectory_transformer.window_length
+        self.decomposer = SpectrumDecomposer(data, trajectory_transformer.ts_length, self.SV_threshold)
+        if svd_flag:
+            return self.estimate_singular_values(data)
+        return self._get_basis(data)
+
     def get_threshold(self, data, selector: str):
 
         selectors = {'median': stats.mode,
@@ -110,75 +111,20 @@ def get_threshold(self, data, selector: str):
 
         return selectors[selector](svd_numbers).mode[0]
 
-    def _transform_one_sample(self, series: np.array, svd_flag: bool = False):
-        trajectory_transformer = HankelMatrix(time_series=series, window_size=self.window_size)
-        data = trajectory_transformer.trajectory_matrix
-        self.ts_length = trajectory_transformer.ts_length
-        if svd_flag:
-            return self.estimate_singular_values(data)
-        return self._get_basis(data)
-
     def estimate_singular_values(self, data):
-        svd = lambda x: ListMonad(self.svd_estimator.rsvd(tensor=x, approximation=self.low_rank_approximation))
-        basis = Either.insert(data).then(svd).value[0]
+        basis = Either.insert(data).then(self.decomposer.svd).value[0]
         spectrum = [s_val for s_val in basis[1] if s_val > 0.001]
         # self.left_approx_sv, self.right_approx_sv = basis[0], basis[2]
         return len(spectrum)
 
     def _get_1d_basis(self, data):
-        data_driven_basis = lambda Monoid: ListMonad(reconstruct_basis(Monoid[0],
-                                                                       Monoid[1],
-                                                                       Monoid[2],
-                                                                       ts_length=self.ts_length))
-        threshold = lambda Monoid: ListMonad([Monoid[0],
-                                              Monoid[1][:self.SV_threshold],
-                                              Monoid[2]])
-        svd = lambda x: ListMonad(self.svd_estimator.rsvd(tensor=x,
-                                                          approximation=self.low_rank_approximation,
-                                                          regularized_rank=self.SV_threshold))
-        basis = Either.insert(data).then(svd).then(threshold).then(data_driven_basis).value[0]
+        basis = Either.insert(data).then(self.decomposer.svd).then(self.decomposer.threshold).then(
+            self.decomposer.data_driven_basis).value[0]
         return np.swapaxes(basis, 1, 0)
 
     def _get_multidim_basis(self, data):
-        rank = round(data[0].shape[0] / 10)
-        beta = data[0].shape[0] / data[0].shape[1]
-
-        tensor_decomposition = lambda x: ListMonad(parafac(tl.tensor(x), rank=rank).factors)
-        multi_threshold = lambda x: singular_value_hard_threshold(singular_values=x,
-                                                                  beta=beta,
-                                                                  threshold=None)
-
-        threshold = lambda Monoid: ListMonad([Monoid[1],
-                                              list(map(multi_threshold, Monoid[0])),
-                                              Monoid[2].T]) if self.n_components is None else ListMonad([Monoid[1][
-                                                                                                         :,
-                                                                                                         :self.n_components],
-                                                                                                         Monoid[0][
-                                                                                                         :,
-                                                                                                         :self.n_components],
-                                                                                                         Monoid[2][
-                                                                                                         :,
-                                                                                                         :self.n_components].T])
-        data_driven_basis = lambda Monoid: ListMonad(reconstruct_basis(Monoid[0],
-                                                                       Monoid[1],
-                                                                       Monoid[2],
-                                                                       ts_length=self.ts_length))
-
         basis = np.array(
-            Either.insert(data).then(tensor_decomposition).then(threshold).then(data_driven_basis).value[0])
-
+            Either.insert(data).then(self.decomposer.tensor_decomposition).then(self.decomposer.multi_threshold).then(
+                self.decomposer.data_driven_basis).value[0])
         basis = basis.reshape(basis.shape[1], -1)
-
         return basis
-
-    def evaluate_derivative(self:
-    class_type,
-                            coefs: np.array,
-                            order: int = 1) -> Tuple[class_type, np.array]:
-        basis = type(self)(
-            domain_range=self.domain_range,
-            n_basis=self.n_basis - order,
-        )
-        derivative_coefs = np.array([np.polyder(x[::-1], order)[::-1] for x in coefs])
-
-        return basis, derivative_coefs
diff --git a/fedot_ind/core/operation/transformation/data/eigen.py b/fedot_ind/core/operation/transformation/data/eigen.py
index fd07c1634..0d8ad0225 100644
--- a/fedot_ind/core/operation/transformation/data/eigen.py
+++ b/fedot_ind/core/operation/transformation/data/eigen.py
@@ -3,75 +3,63 @@
 import copy
 
 
-def weighted_inner_product(F_i, F_j, window_length, ts_length, subseq_length):
+def weighted_inner_product(F_i, F_j, window_length, ts_length):
     # Calculate the weights
     first = list(np.arange(window_length) + 1)
-    second = [ts_length] * (subseq_length - window_length - 1)
+    second = [window_length] * (ts_length - 2*window_length)
     third = list(np.arange(window_length) + 1)[::-1]
     w = np.array(first + second + third)
     return w.dot(F_i * F_j)
 
 
-def calculate_matrix_norms(rank, TS_comps, window_length, ts_length, subseq_length):
-    F_wnorms = np.array(
-        [weighted_inner_product(TS_comps[:, i], TS_comps[:, i], window_length, ts_length, subseq_length) for i in
-         range(rank)])
+def calculate_matrix_norms(TS_comps, window_length, ts_length):
+    r = []
+    for i in range(TS_comps.shape[1]):
+        r.append(weighted_inner_product(TS_comps[:, i], TS_comps[:, i], window_length, ts_length))
+    F_wnorms = np.array(r)
     F_wnorms = F_wnorms ** -0.5
     return F_wnorms
 
 
-def calculate_corr_matrix(rank, TS_comps, F_wnorms, window_length, ts_length, subseq_length):
-    Wcorr = np.identity(rank)
-    components = [i for i in range(rank)]
-    for i in components:
-        for j in range(i + 1, rank):
+def calculate_corr_matrix(TS_comps, F_wnorms, window_length, ts_length):
+    Wcorr = np.identity(TS_comps.shape[1])
+    for i in range(Wcorr.shape[0]):
+        for j in range(i + 1, Wcorr.shape[0]):
             Wcorr[i, j] = abs(
-                weighted_inner_product(TS_comps[:, i], TS_comps[:, j], window_length, ts_length, subseq_length) *
+                weighted_inner_product(TS_comps[:, i], TS_comps[:, j], window_length, ts_length) *
                 F_wnorms[i] * F_wnorms[j])
             Wcorr[j, i] = Wcorr[i, j]
-    return Wcorr, components
+    return Wcorr, [i for i in range(Wcorr.shape[0])]
 
 
-def combine_eigenvectors(TS_comps, rank, window_length, ts_length, subseq_length, correlation_level: float = 0.8):
+def combine_eigenvectors(TS_comps, window_length,  correlation_level: float = 0.8):
     """Calculates the w-correlation matrix for the time series.
 
     Args:
         TS_comps (np.ndarray): The time series components.
-        rank (int): The rank of the time series.
         correlation_level (float): threshold value of Pearson correlation, using for merging eigenvectors.
-        subseq_length (int): The length of TS subseq.
         ts_length (int): The length of TS .
         window_length (int): The length of TS window.
 
 
     """
     combined_components = []
-
+    ts_length = TS_comps.shape[0]
     # Calculated weighted norms
-    F_wnorms = calculate_matrix_norms(rank, TS_comps, window_length, ts_length, subseq_length)
+    F_wnorms = calculate_matrix_norms(TS_comps, window_length, ts_length)
 
     # Calculate Wcorr.
-    Wcorr, components = calculate_corr_matrix(rank, TS_comps, F_wnorms, window_length, ts_length, subseq_length)
-
-    # Calculate Wcorr. and Select Correlated Eigenvectors.
-    corr_dict = {i: [i for i, v in enumerate(Wcorr[:, i]) if v > correlation_level] for i in components}
-    # copy of the dictionary for deleting keys
-    filtred_dict = copy.deepcopy(corr_dict)
-
-    # Select Correlated Eigenvectors.
-    for list_of_corr_vectors in list(corr_dict.values()):
-        final_component = None
-        if len(list_of_corr_vectors) < 2:
-            final_component = TS_comps[:, list_of_corr_vectors[0]]
-            combined_components.append(final_component)
+    Wcorr, components = calculate_corr_matrix(TS_comps, F_wnorms, window_length, ts_length)
+
+    combined_components = []
+    current_group = []
+    for i in range(len(components)):
+        if i == 0 or Wcorr[i, i-1] > correlation_level:
+            current_group.append(TS_comps[:, i])
         else:
-            for corr_vector in list_of_corr_vectors:
-                if corr_vector in filtred_dict.keys():
-                    if final_component is None:
-                        final_component = np.sum(TS_comps[:, list_of_corr_vectors], axis=1)
-                        combined_components.append(final_component)
-                    del filtred_dict[corr_vector]
-
-    combined_components = pd.DataFrame(combined_components).T
+            combined_components.append(np.array(current_group).sum(axis=0))
+            current_group = [TS_comps[:, i]]
+
+
 
     return combined_components
diff --git a/fedot_ind/core/operation/transformation/data/hankel.py b/fedot_ind/core/operation/transformation/data/hankel.py
index 27eb3bb75..d67a25bc1 100644
--- a/fedot_ind/core/operation/transformation/data/hankel.py
+++ b/fedot_ind/core/operation/transformation/data/hankel.py
@@ -1,4 +1,3 @@
-import math
 from typing import Union
 
 import numpy as np
@@ -26,7 +25,7 @@ def __init__(self,
         if self.__window_length is None:
             self.__window_length = round(self.__ts_length * 0.35)
         else:
-            self.__window_length = math.ceil(self.__ts_length * self.__window_length / 100)
+            self.__window_length = round(self.__window_length)
         self.__subseq_length = self.__ts_length - self.__window_length + 1
 
         self.__check_windows_length()
diff --git a/fedot_ind/core/operation/ts_forecasting/__init__.py b/fedot_ind/core/operation/ts_forecasting/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/fedot_ind/core/operation/ts_forecasting/data_driven_ts.py b/fedot_ind/core/operation/ts_forecasting/data_driven_ts.py
new file mode 100644
index 000000000..5dd6c69f0
--- /dev/null
+++ b/fedot_ind/core/operation/ts_forecasting/data_driven_ts.py
@@ -0,0 +1,117 @@
+import math
+from multiprocessing import cpu_count
+from typing import TypeVar, Optional
+
+from fedot.core.data.data import InputData, OutputData
+from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import ModelImplementation
+from fedot.core.operations.operation_parameters import OperationParameters
+from joblib import Parallel, delayed
+from pymonad.either import Either
+from sktime.performance_metrics.forecasting import mean_absolute_scaled_error
+from statsforecast.arima import AutoARIMA
+from statsforecast.models import AutoTheta, AutoETS
+
+from fedot_ind.core.operation.decomposition.SpectrumDecomposition import SpectrumDecomposer
+from fedot_ind.core.operation.transformation.data.hankel import HankelMatrix
+
+class_type = TypeVar("T", bound="DataDrivenBasis")
+
+import numpy as np
+
+try:
+    import seaborn
+except:
+    pass
+from matplotlib.pylab import rcParams
+
+rcParams['figure.figsize'] = 11, 4
+
+
+def MASE(A, F, y_train):
+    return mean_absolute_scaled_error(A, F, y_train=y_train)
+
+
+class DataDrivenForForecastingBasisImplementation(ModelImplementation):
+    """DataDriven basis
+        Example:
+            ts1 = np.random.rand(200)
+            ts2 = np.random.rand(200)
+            ts = [ts1, ts2]
+            bss = DataDrivenBasisImplementation({'n_components': 3, 'window_size': 30})
+            basis_multi = bss._transform(ts)
+            basis_1d = bss._transform(ts1)
+    """
+
+    def __init__(self, params: Optional[OperationParameters] = None):
+        super().__init__(params)
+        self.window_size = params.get('window_size')
+        self.SV_threshold = None
+        self.n_processes = math.ceil(cpu_count() * 0.7) if cpu_count() > 1 else 1
+        self.decomposer = None
+        self.basis = None
+
+    def predict(self, input_data: InputData) -> OutputData:
+        forecast_length = input_data.task.task_params.forecast_length
+        trajectory_transformer = HankelMatrix(time_series=input_data.features, window_size=self.window_size)
+        data = trajectory_transformer.trajectory_matrix
+
+        self.window_size = trajectory_transformer.window_length
+        self.decomposer = SpectrumDecomposer(data,
+                                             trajectory_transformer.ts_length + forecast_length,
+                                             self.SV_threshold)
+        U, s, VT = self.get_svd(data)
+
+        parallel = Parallel(n_jobs=self.n_processes, verbose=0, pre_dispatch="2*n_jobs")
+        new_VT = parallel(delayed(self._predict_component)(sample, forecast_length) for sample in VT[:s.shape[0]])
+        new_VT = np.array(new_VT)
+        basis = self.reconstruct_basis(U, s, new_VT).T
+
+        self.decomposer = SpectrumDecomposer(data,
+                                             trajectory_transformer.ts_length,
+                                             self.SV_threshold)
+        self.train_basis = self.reconstruct_basis(U, s, VT).T
+
+        return np.array(basis).sum(axis=0)[-forecast_length:]
+
+    def fit(self, input_data: InputData):
+        pass
+
+    def predict_for_fit(self, input_data: InputData) -> OutputData:
+        trajectory_transformer = HankelMatrix(time_series=input_data.features, window_size=self.window_size)
+        data = trajectory_transformer.trajectory_matrix
+        self.decomposer = SpectrumDecomposer(data, trajectory_transformer.ts_length)
+
+        self.window_size = trajectory_transformer.window_length
+        self.SV_threshold = self.estimate_singular_values(data)
+        self.decomposer = SpectrumDecomposer(data,
+                                             trajectory_transformer.ts_length,
+                                             self.SV_threshold)
+        U, s, VT = self.get_svd(data)
+        basis = self.reconstruct_basis(U, s, VT).T
+        reconstructed_features = np.array(basis).sum(axis=0)
+        return reconstructed_features
+
+    def estimate_singular_values(self, data):
+        basis = Either.insert(data).then(self.decomposer.svd).value[0]
+        spectrum = [s_val for s_val in basis[1] if s_val > 0.001]
+        # self.left_approx_sv, self.right_approx_sv = basis[0], basis[2]
+        return len(spectrum)
+
+    def _predict_component(self, comp: np.array, forecast_length: int):
+        model_theta = AutoTheta()
+        model_arima = AutoARIMA()
+        model_ets = AutoETS()
+        forecast = []
+        for model in [model_theta, model_arima, model_ets]:
+            model.fit(comp)
+            p = model.predict(forecast_length)['mean']
+            forecast.append(p)
+        forecast = np.median(np.array(forecast), axis=0)
+        return np.concatenate([comp, forecast])
+
+    def get_svd(self, data):
+        components = Either.insert(data).then(self.decomposer.svd).then(self.decomposer.threshold).value[0]
+        return components
+
+    def reconstruct_basis(self, U, s, VT):
+        return Either.insert([U, s, VT]).then(self.decomposer.data_driven_basis).value[0]
diff --git a/fedot_ind/core/repository/data/data_operation_repository.json b/fedot_ind/core/repository/data/data_operation_repository.json
deleted file mode 100644
index 88a420e79..000000000
--- a/fedot_ind/core/repository/data/data_operation_repository.json
+++ /dev/null
@@ -1,590 +0,0 @@
-{
-  "metadata": {
-    "data_sources": {
-      "tasks": "[TaskTypesEnum.classification, TaskTypesEnum.regression, TaskTypesEnum.clustering, TaskTypesEnum.ts_forecasting]",
-      "accepted_node_types": [
-        "PrimaryNode"
-      ],
-      "forbidden_node_types": "[]",
-      "strategies": [
-        "fedot.core.operations.evaluation.data_source",
-        "DataSourceStrategy"
-      ],
-      "tags": [
-        "non-default",
-        "data_source"
-      ],
-      "description": "Implementations of data sources in multi-modal pipelines"
-    },
-    "custom_preprocessing": {
-      "tasks": "[TaskTypesEnum.classification, TaskTypesEnum.regression, TaskTypesEnum.clustering, TaskTypesEnum.ts_forecasting]",
-      "input_type": "[DataTypesEnum.table, DataTypesEnum.ts]",
-      "output_type": "[DataTypesEnum.table, DataTypesEnum.ts]",
-      "accepted_node_types": [
-        "any"
-      ],
-      "forbidden_node_types": "[]",
-      "strategies": [
-        "fedot.core.operations.evaluation.common_preprocessing",
-        "FedotPreprocessingStrategy"
-      ],
-      "tags": [
-        "sklearn"
-      ],
-      "description": "Implementations of the preprocessing from scikit-learn framework"
-    },
-    "sklearn_categorical": {
-      "tasks": "[TaskTypesEnum.classification, TaskTypesEnum.regression, TaskTypesEnum.clustering]",
-      "input_type": "[DataTypesEnum.table]",
-      "output_type": "[DataTypesEnum.table]",
-      "accepted_node_types": [
-        "primary"
-      ],
-      "forbidden_node_types": "[]",
-      "strategies": [
-        "fedot.core.operations.evaluation.common_preprocessing",
-        "FedotPreprocessingStrategy"
-      ],
-      "tags": [
-        "sklearn"
-      ],
-      "description": "Implementations of OneHot encoding (etc.) from scikit-learn framework"
-    },
-    "dimension_transformation": {
-      "tasks": "[TaskTypesEnum.classification, TaskTypesEnum.regression, TaskTypesEnum.clustering, TaskTypesEnum.ts_forecasting]",
-      "input_type": "[DataTypesEnum.table]",
-      "output_type": "[DataTypesEnum.table]",
-      "accepted_node_types": [
-        "any"
-      ],
-      "forbidden_node_types": "[]",
-      "strategies": [
-        "fedot.core.operations.evaluation.common_preprocessing",
-        "FedotPreprocessingStrategy"
-      ],
-      "tags": [
-        "sklearn"
-      ],
-      "description": "Implementations of the dimensionality transformation operations (e.g. PCA) from scikit-learn framework"
-    },
-    "regression_preprocessing": {
-      "tasks": "[TaskTypesEnum.regression, TaskTypesEnum.ts_forecasting]",
-      "input_type": "[DataTypesEnum.table]",
-      "output_type": "[DataTypesEnum.table]",
-      "accepted_node_types": [
-        "any"
-      ],
-      "forbidden_node_types": "[]",
-      "strategies": [
-        "fedot.core.operations.evaluation.regression",
-        "FedotRegressionPreprocessingStrategy"
-      ],
-      "tags": [
-        "sklearn"
-      ],
-      "description": "Implementations of the feature selection operations and robust data filtering from scikit-learn framework for regression task"
-    },
-    "classification_preprocessing": {
-      "tasks": "[TaskTypesEnum.classification]",
-      "input_type": "[DataTypesEnum.table]",
-      "output_type": "[DataTypesEnum.table]",
-      "accepted_node_types": [
-        "any"
-      ],
-      "forbidden_node_types": "[]",
-      "strategies": [
-        "fedot.core.operations.evaluation.classification",
-        "FedotClassificationPreprocessingStrategy"
-      ],
-      "tags": [
-        "sklearn"
-      ],
-      "description": "Implementations of the feature selection operations and robust data filtering from scikit-learn framework for classification task"
-    },
-    "custom_time_series_transformation": {
-      "tasks": "[TaskTypesEnum.ts_forecasting]",
-      "input_type": "[DataTypesEnum.ts, DataTypesEnum.multi_ts]",
-      "output_type": "[DataTypesEnum.ts, DataTypesEnum.table]",
-      "accepted_node_types": [
-        "any"
-      ],
-      "forbidden_node_types": "[]",
-      "strategies": [
-        "fedot.core.operations.evaluation.time_series",
-        "FedotTsTransformingStrategy"
-      ],
-      "tags": [
-        "time_series"
-      ],
-      "description": "Preprocessing for converting uni-variate time-series into table form or transform it"
-    },
-    "text_preprocessing": {
-      "tasks": "[TaskTypesEnum.classification]",
-      "input_type": "[DataTypesEnum.text]",
-      "output_type": "[DataTypesEnum.text, DataTypesEnum.table]",
-      "accepted_node_types": [
-        "any"
-      ],
-      "forbidden_node_types": "[]",
-      "strategies": [
-        "fedot.core.operations.evaluation.text",
-        "FedotTextPreprocessingStrategy"
-      ],
-      "tags": [
-        "text",
-        "non-default"
-      ],
-      "description": "Preprocessing operations for text classification"
-    },
-    "text_preprocessing_sklearn": {
-      "description": "Text preprocessing sklearn",
-      "forbidden_node_types": "[]",
-      "input_type": "[DataTypesEnum.text]",
-      "output_type": "[DataTypesEnum.table]",
-      "strategies": [
-        "fedot.core.operations.evaluation.text",
-        "SkLearnTextVectorizeStrategy"
-      ],
-      "tags": [
-        "text",
-        "nlp",
-        "non-default"
-      ],
-      "tasks": "[TaskTypesEnum.classification]"
-    },
-    "text_classification_gensim": {
-      "description": "Text classification",
-      "forbidden_node_types": [
-        "secondary"
-      ],
-      "input_type": "[DataTypesEnum.text]",
-      "output_type": "[DataTypesEnum.table]",
-      "strategies": [
-        "fedot.core.operations.evaluation.text",
-        "GensimTextVectorizeStrategy"
-      ],
-      "tags": [
-        "text",
-        "nlp",
-        "non-default"
-      ],
-      "tasks": "[TaskTypesEnum.classification]"
-    },
-    "industrial_preprocessing": {
-      "tasks": "[TaskTypesEnum.classification]",
-      "input_type": "[DataTypesEnum.image]",
-      "output_type": "[DataTypesEnum.image, DataTypesEnum.table]",
-      "accepted_node_types": [
-        "any"
-      ],
-      "forbidden_node_types": "[]",
-      "strategies": [
-        "core.operation.interfaces.industrial_preprocessing_strategy",
-        "IndustrialPreprocessingStrategy"
-      ],
-      "tags": [
-      ],
-      "description": "Implementations of industrial preprocessing operations"
-    }
-  },
-  "operations": {
-    "data_driven_basis": {
-      "meta": "industrial_preprocessing",
-      "presets": [
-        "fast_train"
-      ],
-      "tags": [
-        "basis"
-      ]
-    },
-    "wavelet_basis": {
-      "meta": "industrial_preprocessing",
-      "presets": [
-        "fast_train"
-      ],
-      "tags": [
-        "basis"
-      ]
-    },
-    "fourier_basis": {
-      "meta": "industrial_preprocessing",
-      "presets": [
-        "fast_train"
-      ],
-      "tags": [
-        "basis"
-      ]
-    },
-    "topological_extractor": {
-      "meta": "industrial_preprocessing",
-      "presets": [
-        "fast_train"
-      ],
-      "tags": [
-        "extractor"
-      ]
-    },
-    "quantile_extractor": {
-      "meta": "industrial_preprocessing",
-      "presets": [
-        "fast_train"
-      ],
-      "tags": [
-        "extractor"
-      ]
-    },
-    "signal_extractor": {
-      "meta": "industrial_preprocessing",
-      "presets": [
-        "fast_train"
-      ],
-      "tags": [
-         "extractor"
-      ]
-    },
-    "recurrence_extractor": {
-      "meta": "industrial_preprocessing",
-      "presets": [
-        "fast_train"
-      ],
-      "tags": [
-        "extractor"
-      ]
-    },
-    "scaling": {
-      "meta": "custom_preprocessing",
-      "presets": [
-        "fast_train",
-        "ts",
-        "*tree"
-      ],
-      "tags": [
-        "simple",
-        "feature_scaling"
-      ]
-    },
-    "normalization": {
-      "meta": "custom_preprocessing",
-      "presets": [
-        "fast_train",
-        "ts",
-        "*tree"
-      ],
-      "tags": [
-        "simple",
-        "feature_scaling"
-      ]
-    },
-    "simple_imputation": {
-      "meta": "custom_preprocessing",
-      "presets": [
-        "fast_train",
-        "*tree"
-      ],
-      "tags": [
-        "simple",
-        "imputation",
-        "categorical-ignore",
-        "non-default"
-      ]
-    },
-    "pca": {
-      "meta": "dimension_transformation",
-      "presets": [
-        "fast_train",
-        "ts",
-        "*tree"
-      ],
-      "tags": [
-        "linear",
-        "dimensionality_transforming",
-        "correct_params",
-        "non_applicable_for_ts",
-        "feature_reduction"
-      ]
-    },
-    "kernel_pca": {
-      "meta": "dimension_transformation",
-      "presets": [
-        "ts",
-        "*tree"
-      ],
-      "tags": [
-        "non_linear",
-        "dimensionality_transforming",
-        "correct_params",
-        "non_applicable_for_ts",
-        "non-default",
-        "feature_reduction"
-      ]
-    },
-    "fast_ica": {
-      "meta": "dimension_transformation",
-      "presets": [
-        "ts",
-        "*tree"
-      ],
-      "tags": [
-        "non_linear",
-        "dimensionality_transforming",
-        "correct_params",
-        "non_applicable_for_ts",
-        "feature_reduction"
-      ]
-    },
-    "poly_features": {
-      "meta": "dimension_transformation",
-      "tags": [
-        "non_linear",
-        "dimensionality_transforming",
-        "non_applicable_for_ts",
-        "feature_engineering"
-      ]
-    },
-    "one_hot_encoding": {
-      "meta": "sklearn_categorical",
-      "tags": [
-        "encoding",
-        "categorical",
-        "dimensionality_transforming",
-        "non_applicable_for_ts",
-        "non-default"
-      ]
-    },
-    "label_encoding": {
-      "meta": "sklearn_categorical",
-      "presets": [
-        "fast_train",
-        "*tree"
-      ],
-      "tags": [
-        "encoding",
-        "categorical",
-        "non-default",
-        "non_applicable_for_ts"
-      ]
-    },
-    "ransac_lin_reg": {
-      "meta": "regression_preprocessing",
-      "presets": [
-        "fast_train",
-        "*tree"
-      ],
-      "tags": [
-        "affects_target",
-        "linear",
-        "filtering",
-        "correct_params",
-        "non_applicable_for_ts"
-      ]
-    },
-    "ransac_non_lin_reg": {
-      "meta": "regression_preprocessing",
-      "presets": [
-        "fast_train",
-        "*tree"
-      ],
-      "tags": [
-        "affects_target",
-        "non_linear",
-        "filtering",
-        "correct_params",
-        "non_applicable_for_ts"
-      ]
-    },
-    "isolation_forest_reg": {
-      "meta": "regression_preprocessing",
-      "tags": [
-        "non_linear",
-        "filtering"
-      ]
-    },
-    "isolation_forest_class": {
-      "meta": "classification_preprocessing",
-      "tags": [
-        "non_linear",
-        "filtering"
-      ]
-    },
-    "rfe_lin_reg": {
-      "meta": "regression_preprocessing",
-      "tags": [
-        "linear",
-        "feature_selection",
-        "non_applicable_for_ts",
-        "non-default"
-      ]
-    },
-    "rfe_non_lin_reg": {
-      "meta": "regression_preprocessing",
-      "tags": [
-        "non_linear",
-        "feature_selection",
-        "non_applicable_for_ts",
-        "non-default"
-      ]
-    },
-    "rfe_lin_class": {
-      "meta": "classification_preprocessing",
-      "tags": [
-        "linear",
-        "feature_selection",
-        "non-default"
-      ]
-    },
-    "rfe_non_lin_class": {
-      "meta": "classification_preprocessing",
-      "tags": [
-        "non_linear",
-        "feature_selection",
-        "non-default"
-      ]
-    },
-    "lagged": {
-      "meta": "custom_time_series_transformation",
-      "presets": [
-        "fast_train",
-        "ts"
-      ],
-      "tags": [
-        "affects_target",
-        "dimensionality_transforming",
-        "non_lagged",
-        "correct_params",
-        "ts_to_table"
-      ],
-      "input_type": "[DataTypesEnum.multi_ts, DataTypesEnum.ts]"
-    },
-    "sparse_lagged": {
-      "meta": "custom_time_series_transformation",
-      "presets": [
-        "fast_train",
-        "ts"
-      ],
-      "tags": [
-        "affects_target",
-        "dimensionality_transforming",
-        "non_lagged",
-        "correct_params",
-        "ts_to_table"
-      ],
-      "input_type": "[DataTypesEnum.multi_ts, DataTypesEnum.ts]"
-    },
-    "smoothing": {
-      "meta": "custom_time_series_transformation",
-      "presets": [
-        "fast_train",
-        "ts"
-      ],
-      "tags": [
-        "simple",
-        "smoothing",
-        "non_lagged"
-      ],
-      "input_type": "[DataTypesEnum.multi_ts, DataTypesEnum.ts]"
-    },
-    "gaussian_filter": {
-      "meta": "custom_time_series_transformation",
-      "presets": [
-        "fast_train",
-        "ts"
-      ],
-      "tags": [
-        "simple",
-        "smoothing",
-        "non_lagged"
-      ],
-      "input_type": "[DataTypesEnum.multi_ts, DataTypesEnum.ts]"
-    },
-    "diff_filter": {
-      "meta": "custom_time_series_transformation",
-      "presets": [
-        "fast_train",
-        "ts"
-      ],
-      "tags": [
-        "differential",
-        "non_lagged",
-        "correct_params",
-        "ts_to_ts"
-      ],
-      "input_type": "[DataTypesEnum.multi_ts, DataTypesEnum.ts]"
-    },
-    "cut": {
-      "meta": "custom_time_series_transformation",
-      "presets": [
-        "fast_train",
-        "ts"
-      ],
-      "tags": [
-        "simple",
-        "cutting",
-        "correct_params",
-        "non_lagged",
-        "ts_to_ts"
-      ],
-      "input_type": "[DataTypesEnum.multi_ts, DataTypesEnum.ts]"
-    },
-    "exog_ts": {
-      "meta": "custom_time_series_transformation",
-      "tags": [
-        "simple",
-        "non_lagged",
-        "ts_to_table"
-      ]
-    },
-    "text_clean": {
-      "meta": "text_preprocessing",
-      "tags": [
-        "non_applicable_for_ts"
-      ]
-    },
-    "cntvect": {
-      "meta": "text_preprocessing_sklearn",
-      "tags": [
-        "non-default"
-      ]
-    },
-    "tfidf": {
-      "meta": "text_preprocessing_sklearn",
-      "tags": [
-        "non-default"
-      ]
-    },
-    "word2vec_pretrained": {
-      "meta": "text_classification_gensim",
-      "tags": [
-        "non-default"
-      ]
-    },
-    "decompose": {
-      "meta": "regression_preprocessing",
-      "presets": [
-        "fast_train",
-        "ts",
-        "*tree"
-      ],
-      "tags": [
-        "non-default",
-        "decompose"
-      ]
-    },
-    "class_decompose": {
-      "meta": "classification_preprocessing",
-      "presets": [
-        "fast_train",
-        "*tree"
-      ],
-      "tags": [
-        "non-default",
-        "decompose"
-      ]
-    },
-    "resample": {
-      "meta": "classification_preprocessing",
-      "tags": [
-        "imbalanced"
-      ]
-    }
-  }
-}
diff --git a/fedot_ind/core/repository/data/default_operation_params.json b/fedot_ind/core/repository/data/default_operation_params.json
index c29637ff5..773567e06 100644
--- a/fedot_ind/core/repository/data/default_operation_params.json
+++ b/fedot_ind/core/repository/data/default_operation_params.json
@@ -179,5 +179,8 @@
     "max_te_dimension": 5,
     "max_te_time_delay": 2,
     "stride": 1
+  },
+  "data_driven_basis_for_forecasting": {
+    "window_size": 30
   }
 }
\ No newline at end of file
diff --git a/fedot_ind/core/repository/data/industrial_data_operation_repository.json b/fedot_ind/core/repository/data/industrial_data_operation_repository.json
index 33322c4b6..a0c984245 100644
--- a/fedot_ind/core/repository/data/industrial_data_operation_repository.json
+++ b/fedot_ind/core/repository/data/industrial_data_operation_repository.json
@@ -1,5 +1,175 @@
 {
   "metadata": {
+    "data_sources": {
+      "tasks": "[TaskTypesEnum.classification, TaskTypesEnum.regression, TaskTypesEnum.clustering, TaskTypesEnum.ts_forecasting]",
+      "accepted_node_types": [
+        "PrimaryNode"
+      ],
+      "forbidden_node_types": "[]",
+      "strategies": [
+        "fedot.core.operations.evaluation.data_source",
+        "DataSourceStrategy"
+      ],
+      "tags": [
+        "non-default",
+        "data_source"
+      ],
+      "description": "Implementations of data sources in multi-modal pipelines"
+    },
+    "custom_preprocessing": {
+      "tasks": "[TaskTypesEnum.classification, TaskTypesEnum.regression, TaskTypesEnum.clustering, TaskTypesEnum.ts_forecasting]",
+      "input_type": "[DataTypesEnum.table, DataTypesEnum.ts]",
+      "output_type": "[DataTypesEnum.table, DataTypesEnum.ts]",
+      "accepted_node_types": [
+        "any"
+      ],
+      "forbidden_node_types": "[]",
+      "strategies": [
+        "fedot.core.operations.evaluation.common_preprocessing",
+        "FedotPreprocessingStrategy"
+      ],
+      "tags": [
+        "sklearn"
+      ],
+      "description": "Implementations of the preprocessing from scikit-learn framework"
+    },
+    "sklearn_categorical": {
+      "tasks": "[TaskTypesEnum.classification, TaskTypesEnum.regression, TaskTypesEnum.clustering]",
+      "input_type": "[DataTypesEnum.table]",
+      "output_type": "[DataTypesEnum.table]",
+      "accepted_node_types": [
+        "primary"
+      ],
+      "forbidden_node_types": "[]",
+      "strategies": [
+        "fedot.core.operations.evaluation.common_preprocessing",
+        "FedotPreprocessingStrategy"
+      ],
+      "tags": [
+        "sklearn"
+      ],
+      "description": "Implementations of OneHot encoding (etc.) from scikit-learn framework"
+    },
+    "dimension_transformation": {
+      "tasks": "[TaskTypesEnum.classification, TaskTypesEnum.regression, TaskTypesEnum.clustering, TaskTypesEnum.ts_forecasting]",
+      "input_type": "[DataTypesEnum.table]",
+      "output_type": "[DataTypesEnum.table]",
+      "accepted_node_types": [
+        "any"
+      ],
+      "forbidden_node_types": "[]",
+      "strategies": [
+        "fedot.core.operations.evaluation.common_preprocessing",
+        "FedotPreprocessingStrategy"
+      ],
+      "tags": [
+        "sklearn"
+      ],
+      "description": "Implementations of the dimensionality transformation operations (e.g. PCA) from scikit-learn framework"
+    },
+    "regression_preprocessing": {
+      "tasks": "[TaskTypesEnum.regression, TaskTypesEnum.ts_forecasting]",
+      "input_type": "[DataTypesEnum.table]",
+      "output_type": "[DataTypesEnum.table]",
+      "accepted_node_types": [
+        "any"
+      ],
+      "forbidden_node_types": "[]",
+      "strategies": [
+        "fedot.core.operations.evaluation.regression",
+        "FedotRegressionPreprocessingStrategy"
+      ],
+      "tags": [
+        "sklearn"
+      ],
+      "description": "Implementations of the feature selection operations and robust data filtering from scikit-learn framework for regression task"
+    },
+    "classification_preprocessing": {
+      "tasks": "[TaskTypesEnum.classification]",
+      "input_type": "[DataTypesEnum.table]",
+      "output_type": "[DataTypesEnum.table]",
+      "accepted_node_types": [
+        "any"
+      ],
+      "forbidden_node_types": "[]",
+      "strategies": [
+        "fedot.core.operations.evaluation.classification",
+        "FedotClassificationPreprocessingStrategy"
+      ],
+      "tags": [
+        "sklearn"
+      ],
+      "description": "Implementations of the feature selection operations and robust data filtering from scikit-learn framework for classification task"
+    },
+    "custom_time_series_transformation": {
+      "tasks": "[TaskTypesEnum.ts_forecasting]",
+      "input_type": "[DataTypesEnum.ts, DataTypesEnum.multi_ts]",
+      "output_type": "[DataTypesEnum.ts, DataTypesEnum.table]",
+      "accepted_node_types": [
+        "any"
+      ],
+      "forbidden_node_types": "[]",
+      "strategies": [
+        "fedot.core.operations.evaluation.time_series",
+        "FedotTsTransformingStrategy"
+      ],
+      "tags": [
+        "time_series"
+      ],
+      "description": "Preprocessing for converting uni-variate time-series into table form or transform it"
+    },
+    "text_preprocessing": {
+      "tasks": "[TaskTypesEnum.classification]",
+      "input_type": "[DataTypesEnum.text]",
+      "output_type": "[DataTypesEnum.text, DataTypesEnum.table]",
+      "accepted_node_types": [
+        "any"
+      ],
+      "forbidden_node_types": "[]",
+      "strategies": [
+        "fedot.core.operations.evaluation.text",
+        "FedotTextPreprocessingStrategy"
+      ],
+      "tags": [
+        "text",
+        "non-default"
+      ],
+      "description": "Preprocessing operations for text classification"
+    },
+    "text_preprocessing_sklearn": {
+      "description": "Text preprocessing sklearn",
+      "forbidden_node_types": "[]",
+      "input_type": "[DataTypesEnum.text]",
+      "output_type": "[DataTypesEnum.table]",
+      "strategies": [
+        "fedot.core.operations.evaluation.text",
+        "SkLearnTextVectorizeStrategy"
+      ],
+      "tags": [
+        "text",
+        "nlp",
+        "non-default"
+      ],
+      "tasks": "[TaskTypesEnum.classification]"
+    },
+    "text_classification_gensim": {
+      "description": "Text classification",
+      "forbidden_node_types": [
+        "secondary"
+      ],
+      "input_type": "[DataTypesEnum.text]",
+      "output_type": "[DataTypesEnum.table]",
+      "strategies": [
+        "fedot.core.operations.evaluation.text",
+        "GensimTextVectorizeStrategy"
+      ],
+      "tags": [
+        "text",
+        "nlp",
+        "non-default"
+      ],
+      "tasks": "[TaskTypesEnum.classification]"
+    },
     "industrial_preprocessing": {
       "tasks": "[TaskTypesEnum.classification]",
       "input_type": "[DataTypesEnum.image]",
@@ -15,9 +185,37 @@
       "tags": [
       ],
       "description": "Implementations of industrial preprocessing operations"
+    },
+    "industrial_decomposition_for_forecasting": {
+      "tasks": "[TaskTypesEnum.ts_forecasting]",
+      "input_type": "[DataTypesEnum.ts]",
+      "output_type": "[DataTypesEnum.table]",
+      "accepted_node_types": [
+        "any"
+      ],
+      "forbidden_node_types": "[]",
+      "strategies": [
+        "fedot_ind.core.operation.interfaces.industrial_decomposition_for_forecasting_strategy",
+        "IndustrialDecompositionForForecastingStrategy"
+      ],
+      "tags": [
+      ],
+      "description": "Implementations of industrial operations for decomposition of time series for forecasting it's parts"
     }
   },
   "operations": {
+    "data_source_ts": {
+      "meta": "data_sources",
+      "tags": [
+        "data_source_ts",
+        "non_lagged",
+        "non-default",
+        "nans-ignore",
+        "categorical-ignore"
+      ],
+      "input_type": "[DataTypesEnum.ts]",
+      "output_type": "[DataTypesEnum.ts]"
+    },
     "data_driven_basis": {
       "meta": "industrial_preprocessing",
       "presets": [
@@ -69,7 +267,7 @@
         "fast_train"
       ],
       "tags": [
-         "extractor"
+        "extractor"
       ]
     },
     "recurrence_extractor": {
@@ -89,6 +287,350 @@
       "tags": [
         "cat_features"
       ]
+    },
+    "data_driven_basis_for_forecasting": {
+      "meta": "industrial_decomposition_for_forecasting",
+      "presets": [
+        "fast_train"
+      ],
+      "tags": [
+        "decomposition"
+      ]
+    },
+    "scaling": {
+      "meta": "custom_preprocessing",
+      "presets": [
+        "fast_train",
+        "ts",
+        "*tree"
+      ],
+      "tags": [
+        "simple",
+        "feature_scaling"
+      ]
+    },
+    "normalization": {
+      "meta": "custom_preprocessing",
+      "presets": [
+        "fast_train",
+        "ts",
+        "*tree"
+      ],
+      "tags": [
+        "simple",
+        "feature_scaling"
+      ]
+    },
+    "simple_imputation": {
+      "meta": "custom_preprocessing",
+      "presets": [
+        "fast_train",
+        "*tree"
+      ],
+      "tags": [
+        "simple",
+        "imputation",
+        "categorical-ignore",
+        "non-default"
+      ]
+    },
+    "pca": {
+      "meta": "dimension_transformation",
+      "presets": [
+        "fast_train",
+        "ts",
+        "*tree"
+      ],
+      "tags": [
+        "linear",
+        "dimensionality_transforming",
+        "correct_params",
+        "non_applicable_for_ts",
+        "feature_reduction"
+      ]
+    },
+    "kernel_pca": {
+      "meta": "dimension_transformation",
+      "presets": [
+        "ts",
+        "*tree"
+      ],
+      "tags": [
+        "non_linear",
+        "dimensionality_transforming",
+        "correct_params",
+        "non_applicable_for_ts",
+        "non-default",
+        "feature_reduction"
+      ]
+    },
+    "fast_ica": {
+      "meta": "dimension_transformation",
+      "presets": [
+        "ts",
+        "*tree"
+      ],
+      "tags": [
+        "non_linear",
+        "dimensionality_transforming",
+        "correct_params",
+        "non_applicable_for_ts",
+        "feature_reduction"
+      ]
+    },
+    "poly_features": {
+      "meta": "dimension_transformation",
+      "tags": [
+        "non_linear",
+        "dimensionality_transforming",
+        "non_applicable_for_ts",
+        "feature_engineering"
+      ]
+    },
+    "one_hot_encoding": {
+      "meta": "sklearn_categorical",
+      "tags": [
+        "encoding",
+        "categorical",
+        "dimensionality_transforming",
+        "non_applicable_for_ts",
+        "non-default"
+      ]
+    },
+    "label_encoding": {
+      "meta": "sklearn_categorical",
+      "presets": [
+        "fast_train",
+        "*tree"
+      ],
+      "tags": [
+        "encoding",
+        "categorical",
+        "non-default",
+        "non_applicable_for_ts"
+      ]
+    },
+    "ransac_lin_reg": {
+      "meta": "regression_preprocessing",
+      "presets": [
+        "fast_train",
+        "*tree"
+      ],
+      "tags": [
+        "affects_target",
+        "linear",
+        "filtering",
+        "correct_params",
+        "non_applicable_for_ts"
+      ]
+    },
+    "ransac_non_lin_reg": {
+      "meta": "regression_preprocessing",
+      "presets": [
+        "fast_train",
+        "*tree"
+      ],
+      "tags": [
+        "affects_target",
+        "non_linear",
+        "filtering",
+        "correct_params",
+        "non_applicable_for_ts"
+      ]
+    },
+    "isolation_forest_reg": {
+      "meta": "regression_preprocessing",
+      "tags": [
+        "non_linear",
+        "filtering"
+      ]
+    },
+    "isolation_forest_class": {
+      "meta": "classification_preprocessing",
+      "tags": [
+        "non_linear",
+        "filtering"
+      ]
+    },
+    "rfe_lin_reg": {
+      "meta": "regression_preprocessing",
+      "tags": [
+        "linear",
+        "feature_selection",
+        "non_applicable_for_ts",
+        "non-default"
+      ]
+    },
+    "rfe_non_lin_reg": {
+      "meta": "regression_preprocessing",
+      "tags": [
+        "non_linear",
+        "feature_selection",
+        "non_applicable_for_ts",
+        "non-default"
+      ]
+    },
+    "rfe_lin_class": {
+      "meta": "classification_preprocessing",
+      "tags": [
+        "linear",
+        "feature_selection",
+        "non-default"
+      ]
+    },
+    "rfe_non_lin_class": {
+      "meta": "classification_preprocessing",
+      "tags": [
+        "non_linear",
+        "feature_selection",
+        "non-default"
+      ]
+    },
+    "lagged": {
+      "meta": "custom_time_series_transformation",
+      "presets": [
+        "fast_train",
+        "ts"
+      ],
+      "tags": [
+        "affects_target",
+        "dimensionality_transforming",
+        "non_lagged",
+        "correct_params",
+        "ts_to_table"
+      ],
+      "input_type": "[DataTypesEnum.multi_ts, DataTypesEnum.ts]"
+    },
+    "sparse_lagged": {
+      "meta": "custom_time_series_transformation",
+      "presets": [
+        "fast_train",
+        "ts"
+      ],
+      "tags": [
+        "affects_target",
+        "dimensionality_transforming",
+        "non_lagged",
+        "correct_params",
+        "ts_to_table"
+      ],
+      "input_type": "[DataTypesEnum.multi_ts, DataTypesEnum.ts]"
+    },
+    "smoothing": {
+      "meta": "custom_time_series_transformation",
+      "presets": [
+        "fast_train",
+        "ts"
+      ],
+      "tags": [
+        "simple",
+        "smoothing",
+        "non_lagged"
+      ],
+      "input_type": "[DataTypesEnum.multi_ts, DataTypesEnum.ts]"
+    },
+    "gaussian_filter": {
+      "meta": "custom_time_series_transformation",
+      "presets": [
+        "fast_train",
+        "ts"
+      ],
+      "tags": [
+        "simple",
+        "smoothing",
+        "non_lagged"
+      ],
+      "input_type": "[DataTypesEnum.multi_ts, DataTypesEnum.ts]"
+    },
+    "diff_filter": {
+      "meta": "custom_time_series_transformation",
+      "presets": [
+        "fast_train",
+        "ts"
+      ],
+      "tags": [
+        "differential",
+        "non_lagged",
+        "correct_params",
+        "ts_to_ts"
+      ],
+      "input_type": "[DataTypesEnum.multi_ts, DataTypesEnum.ts]"
+    },
+    "cut": {
+      "meta": "custom_time_series_transformation",
+      "presets": [
+        "fast_train",
+        "ts"
+      ],
+      "tags": [
+        "simple",
+        "cutting",
+        "correct_params",
+        "non_lagged",
+        "ts_to_ts"
+      ],
+      "input_type": "[DataTypesEnum.multi_ts, DataTypesEnum.ts]"
+    },
+    "exog_ts": {
+      "meta": "custom_time_series_transformation",
+      "tags": [
+        "simple",
+        "non_lagged",
+        "ts_to_table"
+      ]
+    },
+    "text_clean": {
+      "meta": "text_preprocessing",
+      "tags": [
+        "non_applicable_for_ts"
+      ]
+    },
+    "cntvect": {
+      "meta": "text_preprocessing_sklearn",
+      "tags": [
+        "non-default"
+      ]
+    },
+    "tfidf": {
+      "meta": "text_preprocessing_sklearn",
+      "tags": [
+        "non-default"
+      ]
+    },
+    "word2vec_pretrained": {
+      "meta": "text_classification_gensim",
+      "tags": [
+        "non-default"
+      ]
+    },
+    "decompose": {
+      "meta": "regression_preprocessing",
+      "presets": [
+        "fast_train",
+        "ts",
+        "*tree"
+      ],
+      "tags": [
+        "non-default",
+        "decompose"
+      ]
+    },
+    "class_decompose": {
+      "meta": "classification_preprocessing",
+      "presets": [
+        "fast_train",
+        "*tree"
+      ],
+      "tags": [
+        "non-default",
+        "decompose"
+      ]
+    },
+    "resample": {
+      "meta": "classification_preprocessing",
+      "tags": [
+        "imbalanced"
+      ]
     }
   }
 }
diff --git a/fedot_ind/core/repository/initializer_industrial_models.py b/fedot_ind/core/repository/initializer_industrial_models.py
index 9f6e16c12..22c09a9eb 100644
--- a/fedot_ind/core/repository/initializer_industrial_models.py
+++ b/fedot_ind/core/repository/initializer_industrial_models.py
@@ -6,7 +6,6 @@
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.search_space import PipelineSearchSpace
-
 from fedot.core.repository.operation_types_repository import OperationTypesRepository, get_operations_for_task
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from golem.core.dag.verification_rules import ERROR_PREFIX
@@ -25,7 +24,7 @@ def add_preprocessing(pipeline: Pipeline, **kwargs) -> Pipeline:
     extractor_model = PipelineNode(random.choice(extractors), nodes_from=[basis_model])
 
     try:
-        node_to_mutate = list(filter(lambda x: x.name  in models, pipeline.nodes))[0]
+        node_to_mutate = list(filter(lambda x: x.name in models, pipeline.nodes))[0]
     except:
         pipeline.show()
     if node_to_mutate.nodes_from:
@@ -98,7 +97,7 @@ def has_no_data_flow_conflicts_in_industrial_pipeline(pipeline: Pipeline):
 
 class IndustrialModels:
     def __init__(self):
-        self.industrial_data_operation_path = pathlib.Path(PROJECT_PATH,                                                         'fedot_ind',
+        self.industrial_data_operation_path = pathlib.Path(PROJECT_PATH, 'fedot_ind',
                                                            'core',
                                                            'repository',
                                                            'data',
@@ -127,4 +126,4 @@ def __exit__(self, exc_type, exc_val, exc_tb):
                                                                       OperationTypesRepository.DEFAULT_DATA_OPERATION_TAGS]}})
         OperationTypesRepository.assign_repo('data_operation', self.base_data_operation_path)
 
-        #setattr(ApiComposer, "_get_default_mutations", _get_default_mutations)
+        # setattr(ApiComposer, "_get_default_mutations", _get_default_mutations)
diff --git a/fedot_ind/core/tuning/search_space.py b/fedot_ind/core/tuning/search_space.py
index b4636d317..dc300c355 100644
--- a/fedot_ind/core/tuning/search_space.py
+++ b/fedot_ind/core/tuning/search_space.py
@@ -1,6 +1,9 @@
-import numpy as np
+from functools import partial
+
 from hyperopt import hp
 
+NESTED_PARAMS_LABEL = 'nested_label'
+
 industrial_search_space = {
     'data_driven_basis':
         {
@@ -27,284 +30,719 @@
          'rec_metric': (hp.choice, [['chebyshev', 'cosine', 'euclidean', 'mahalanobis']])},
     'signal_extractor':
         {'wavelet': {'hyperopt-dist': hp.choice,
-                     'sampling-scope': [['mexh', 'shan', 'morl', 'cmor', 'fbsp', 'db5', 'sym5']]}}
+                     'sampling-scope': [['mexh', 'shan', 'morl', 'cmor', 'fbsp', 'db5', 'sym5']]}},
+    'data_driven_basis_for_forecasting':
+        {
+            'window_size': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [5, 200]}}
 }
 
 
 def get_industrial_search_space(self):
-    parameters_per_operation = {
-        'kmeans': {
-            'n_clusters': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 7]}
-        },
+    parameters_per_operation = {'kmeans': {
+        'n_clusters': {
+            'hyperopt-dist': hp.uniformint,
+            'sampling-scope': [2, 7],
+            'type': 'discrete'}
+    },
         'adareg': {
-
-            'learning_rate': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(1e-3), np.log(1)]},
-            'loss': {'hyperopt-dist': hp.choice, 'sampling-scope': [["linear", "square", "exponential"]]}
+            'learning_rate': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [1e-3, 1],
+                'type': 'continuous'},
+            'loss': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [["linear", "square", "exponential"]],
+                'type': 'categorical'}
         },
         'gbr': {
-
-            'loss': {'hyperopt-dist': hp.choice, 'sampling-scope': [['squared_error', "huber", "quantile"]]},
-            'learning_rate': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(1e-3), np.log(1)]},
-            'max_depth': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 11]},
-            'min_samples_split': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 21]},
-            'min_samples_leaf': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 21]},
-            'subsample': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.05, 1.0]},
-            'max_features': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.05, 1.0]},
-            'alpha': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.75, 0.99]}
+            'loss': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [["ls", "lad", "huber", "quantile"]],
+                'type': 'categorical'},
+            'learning_rate': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [1e-3, 1],
+                'type': 'continuous'},
+            'max_depth': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 11],
+                'type': 'discrete'},
+            'min_samples_split': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [2, 21],
+                'type': 'discrete'},
+            'min_samples_leaf': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 21],
+                'type': 'discrete'},
+            'subsample': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.05, 1.0],
+                'type': 'continuous'},
+            'max_features': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.05, 1.0],
+                'type': 'continuous'},
+            'alpha': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.75, 0.99],
+                'type': 'continuous'}
         },
         'logit': {
-            'C': {'hyperopt-dist': hp.uniform, 'sampling-scope': [1e-2, 10.0]}
+            'C': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [1e-2, 10.0],
+                'type': 'continuous'}
         },
         'rf': {
-            'criterion': {'hyperopt-dist': hp.choice, 'sampling-scope': [["gini", "entropy"]]},
-            'max_features': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.05, 1.0]},
-            'min_samples_split': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 10]},
-            'min_samples_leaf': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 15]},
-            'bootstrap': {'hyperopt-dist': hp.choice, 'sampling-scope': [[True, False]]}
-        },
-        'lasso': {
-            'alpha': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.01, 10.0]}
+            'criterion': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [["gini", "entropy"]],
+                'type': 'categorical'},
+            'max_features': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.05, 1.0],
+                'type': 'continuous'},
+            'min_samples_split': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [2, 10],
+                'type': 'discrete'},
+            'min_samples_leaf': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 15],
+                'type': 'discrete'},
+            'bootstrap': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[True, False]],
+                'type': 'categorical'}
         },
         'ridge': {
-            'alpha': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.01, 10.0]}
+            'alpha': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.01, 10.0],
+                'type': 'continuous'}
+        },
+        'lasso': {
+            'alpha': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.01, 10.0],
+                'type': 'continuous'}
         },
         'rfr': {
-
-            'max_features': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.05, 1.0]},
-            'min_samples_split': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 21]},
-            'min_samples_leaf': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 21]},
-            'bootstrap': {'hyperopt-dist': hp.choice, 'sampling-scope': [[True, False]]}
+            'max_features': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.05, 1.0],
+                'type': 'continuous'},
+            'min_samples_split': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [2, 21],
+                'type': 'discrete'},
+            'min_samples_leaf': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 15],
+                'type': 'discrete'},
+            'bootstrap': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[True, False]],
+                'type': 'categorical'}
         },
         'xgbreg': {
-
-            'max_depth': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 11]},
-            'learning_rate': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(1e-3), np.log(1)]},
-            'subsample': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.05, 1.0]},
-            'min_child_weight': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 21]},
-            'objective': {'hyperopt-dist': hp.choice, 'sampling-scope': [['reg:squarederror']]}
+            'max_depth': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 11],
+                'type': 'discrete'},
+            'learning_rate': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [1e-3, 1],
+                'type': 'continuous'},
+            'subsample': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.05, 1.0],
+                'type': 'continuous'},
+            'min_child_weight': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 21],
+                'type': 'discrete'},
         },
         'xgboost': {
-
-            'max_depth': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 7]},
-            'learning_rate': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(1e-3), np.log(1)]},
-            'subsample': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.05, 0.99]},
-            'min_child_weight': {'hyperopt-dist': hp.uniform, 'sampling-scope': [1, 21]}
+            'max_depth': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 7],
+                'type': 'discrete'},
+            'learning_rate': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [1e-3, 1],
+                'type': 'continuous'},
+            'subsample': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.05, 0.99],
+                'type': 'continuous'},
+            'min_child_weight': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 21],
+                'type': 'discrete'}
         },
         'svr': {
-            'loss': {'hyperopt-dist': hp.choice,
-                     'sampling-scope': [["epsilon_insensitive", "squared_epsilon_insensitive"]]},
-            'tol': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(1e-5), np.log(1e-1)]},
-            'C': {'hyperopt-dist': hp.uniform, 'sampling-scope': [1e-4, 25.0]},
-            'epsilon': {'hyperopt-dist': hp.uniform, 'sampling-scope': [1e-4, 1.0]}
+            'C': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [1e-4, 25.0],
+                'type': 'continuous'},
+            'epsilon': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [1e-4, 1],
+                'type': 'continuous'},
+            'tol': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [1e-5, 1e-1],
+                'type': 'continuous'},
+            'loss': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [["epsilon_insensitive", "squared_epsilon_insensitive"]],
+                'type': 'categorical'}
         },
         'dtreg': {
-            'max_depth': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 11]},
-            'min_samples_split': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 21]},
-            'min_samples_leaf': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 21]}
+            'max_depth': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 11],
+                'type': 'discrete'},
+            'min_samples_split': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [2, 21],
+                'type': 'discrete'},
+            'min_samples_leaf': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 21],
+                'type': 'discrete'}
         },
         'treg': {
-
-            'max_features': (hp.uniform, [0.05, 1.0]),
-            'min_samples_split': (hp.uniformint, [2, 21]),
-            'min_samples_leaf': (hp.uniformint, [1, 21]),
-            'bootstrap': (hp.choice, [[True, False]])
+            'max_features': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.05, 1.0],
+                'type': 'continuous'},
+            'min_samples_split': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [2, 21],
+                'type': 'discrete'},
+            'min_samples_leaf': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 21],
+                'type': 'discrete'},
+            'bootstrap': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[True, False]],
+                'type': 'categorical'}
         },
         'dt': {
-            'max_depth': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 11]},
-            'min_samples_split': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 21]},
-            'min_samples_leaf': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 21]}
+            'max_depth': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 11],
+                'type': 'discrete'},
+            'min_samples_split': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [2, 21],
+                'type': 'discrete'},
+            'min_samples_leaf': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 21],
+                'type': 'discrete'}
         },
         'knnreg': {
-            'n_neighbors': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 50]},
-            'weights': {'hyperopt-dist': hp.choice, 'sampling-scope': [["uniform", "distance"]]},
-            'p': {'hyperopt-dist': hp.choice, 'sampling-scope': [[1, 2]]}
+            'n_neighbors': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 50],
+                'type': 'discrete'},
+            'weights': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [["uniform", "distance"]],
+                'type': 'categorical'},
+            'p': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[1, 2]],
+                'type': 'categorical'}
         },
         'knn': {
-            'n_neighbors': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 50]},
-            'weights': {'hyperopt-dist': hp.choice, 'sampling-scope': [["uniform", "distance"]]},
-            'p': {'hyperopt-dist': hp.choice, 'sampling-scope': [[1, 2]]}
+            'n_neighbors': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 50],
+                'type': 'discrete'},
+            'weights': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [["uniform", "distance"]],
+                'type': 'categorical'},
+            'p': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[1, 2]],
+                'type': 'categorical'}
         },
         'arima': {
-            'p': (hp.uniformint, [1, 7]),
-            'd': (hp.uniformint, [0, 2]),
-            'q': (hp.uniformint, [1, 5])
+            'p': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 7],
+                'type': 'discrete'},
+            'd': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [0, 2],
+                'type': 'discrete'},
+            'q': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 5],
+                'type': 'discrete'}
         },
         'stl_arima': {
-            'p': (hp.uniformint, [1, 7]),
-            'd': (hp.uniformint, [0, 2]),
-            'q': (hp.uniformint, [1, 5]),
-            'period': (hp.uniformint, [1, 365])
+            'p': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 7],
+                'type': 'discrete'},
+            'd': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [0, 2],
+                'type': 'discrete'},
+            'q': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 5],
+                'type': 'discrete'},
+            'period': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 365],
+                'type': 'discrete'}
         },
         'ar': {
-            'lag_1': (hp.uniform, [2, 200]),
-            'lag_2': (hp.uniform, [2, 800])
+            'lag_1': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [2, 200],
+                'type': 'continuous'},
+            'lag_2': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [2, 800],
+                'type': 'continuous'}
         },
         'ets': {
-            'error': (hp.choice, [['add', 'mul']]),
-            'trend': (hp.choice, [[None, 'add', 'mul']]),
-            'seasonal': (hp.choice, [[None, 'add', 'mul']]),
-            'damped_trend': (hp.choice, [[True, False]]),
-            'seasonal_periods': (hp.uniform, [1, 100])
-        },
-        'glm': {'nested_space': (hp.choice, [[
-            {
-                'family': 'gaussian',
-                'link': hp.choice('link_gaussian', ['identity',
-                                                    'inverse_power',
-                                                    'log'])
-            },
-            {
-                'family': 'gamma',
-                'link': hp.choice('link_gamma', ['identity',
-                                                 'inverse_power',
-                                                 'log'])
-            },
-            {
-                'family': 'inverse_gaussian',
-                'link': hp.choice('link_inv_gaussian', ['identity',
-                                                        'inverse_power'])
-            }
+            'error': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [["add", "mul"]],
+                'type': 'categorical'},
+            'trend': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[None, "add", "mul"]],
+                'type': 'categorical'},
+            'seasonal': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[None, "add", "mul"]],
+                'type': 'categorical'},
+            'damped_trend': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[True, False]],
+                'type': 'categorical'},
+            'seasonal_periods': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [1, 100],
+                'type': 'continuous'}
+        },
+        'glm': {
+            NESTED_PARAMS_LABEL: {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[
+                    {
+                        'family': 'gaussian',
+                        'link': hp.choice('link_gaussian', ['identity',
+                                                            'inverse_power',
+                                                            'log'])
+                    },
+                    {
+                        'family': 'gamma',
+                        'link': hp.choice('link_gamma', ['identity',
+                                                         'inverse_power',
+                                                         'log'])
+                    },
+                    {
+                        'family': 'inverse_gaussian',
+                        'link': hp.choice('link_inv_gaussian', ['identity',
+                                                                'inverse_power'])
+                    }
 
-        ]])},
-        'clstm': {
-            'window_size': (hp.uniform, [1, 200]),
-            'hidden_size': (hp.uniform, [20, 200]),
-            'teacher_forcing': (hp.uniform, [0, 1]),
-            'learning_rate': (hp.uniform, [0.0005, 0.005]),
-            'cnn1_kernel_size': (hp.uniformint, [3, 8]),
-            'cnn1_output_size': (hp.choice, [[8, 16, 32, 64]]),
-            'cnn2_kernel_size': (hp.uniformint, [3, 8]),
-            'cnn2_output_size': (hp.choice, [[8, 16, 32, 64]]),
-            'batch_size': (hp.choice, [[64, 128]]),
-            'num_epochs': (hp.choice, [[10, 20, 50, 100]]),
-            'optimizer': (hp.choice, [['adam', 'sgd']]),
-            'loss': (hp.choice, [['mae', 'mse']])
+                ]],
+                'type': 'categorical'}
+        },
+        'cgru': {
+            'hidden_size': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [20, 200],
+                'type': 'continuous'},
+            'learning_rate': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.0005, 0.005],
+                'type': 'continuous'},
+            'cnn1_kernel_size': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [3, 8],
+                'type': 'discrete'},
+            'cnn1_output_size': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[8, 16, 32, 64]],
+                'type': 'categorical'},
+            'cnn2_kernel_size': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [3, 8],
+                'type': 'discrete'},
+            'cnn2_output_size': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[8, 16, 32, 64]],
+                'type': 'categorical'},
+            'batch_size': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[64, 128]],
+                'type': 'categorical'},
+            'num_epochs': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[10, 20, 50, 100]],
+                'type': 'categorical'},
+            'optimizer': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [['adamw', 'sgd']],
+                'type': 'categorical'},
+            'loss': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [['mae', 'mse']],
+                'type': 'categorical'},
         },
         'pca': {
-            'n_components': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.1, 0.99]},
-            'svd_solver': {'hyperopt-dist': hp.choice, 'sampling-scope': [['full']]}
+            'n_components': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.1, 0.99],
+                'type': 'continuous'}
         },
         'kernel_pca': {
-            'n_components': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 20]},
-            'kernel': {'hyperopt-dist': hp.choice,
-                       'sampling-scope': [['linear', 'poly', 'rbf', 'sigmoid', 'cosine', 'precomputed']]}
+            'n_components': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 20],
+                'type': 'discrete'},
+            'kernel': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [['linear', 'poly', 'rbf', 'sigmoid', 'cosine', 'precomputed']],
+                'type': 'categorical'}
         },
         'fast_ica': {
-            'n_components': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 20]},
-            'fun': {'hyperopt-dist': hp.choice, 'sampling-scope': [['logcosh', 'exp', 'cube']]}
+            'n_components': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 20],
+                'type': 'discrete'},
+            'fun': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [['logcosh', 'exp', 'cube']],
+                'type': 'categorical'}
         },
         'ransac_lin_reg': {
-            'min_samples': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.1, 0.9]},
-            'residual_threshold': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(0.1), np.log(1000)]},
-            'max_trials': {'hyperopt-dist': hp.uniform, 'sampling-scope': [50, 500]},
-            'max_skips': {'hyperopt-dist': hp.uniform, 'sampling-scope': [50, 500000]}
+            'min_samples': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.1, 0.9],
+                'type': 'continuous'},
+            'residual_threshold': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [0.1, 1000],
+                'type': 'continuous'},
+            'max_trials': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [50, 500],
+                'type': 'continuous'},
+            'max_skips': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [50, 500000],
+                'type': 'continuous'}
         },
         'ransac_non_lin_reg': {
-            'min_samples': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.1, 0.9]},
-            'residual_threshold': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(0.1), np.log(1000)]},
-            'max_trials': {'hyperopt-dist': hp.uniform, 'sampling-scope': [50, 500]},
-            'max_skips': {'hyperopt-dist': hp.uniform, 'sampling-scope': [50, 500000]}
+            'min_samples': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.1, 0.9],
+                'type': 'continuous'},
+            'residual_threshold': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [0.1, 1000],
+                'type': 'continuous'},
+            'max_trials': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [50, 500],
+                'type': 'continuous'},
+            'max_skips': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [50, 500000],
+                'type': 'continuous'}
         },
         'isolation_forest_reg': {
-            'max_samples': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.05, 0.99]},
-            'max_features': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.05, 0.99]},
-            'bootstrap': {'hyperopt-dist': hp.choice, 'sampling-scope': [[True, False]]}
+            'max_samples': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.05, 0.99],
+                'type': 'continuous'},
+            'max_features': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.05, 0.99],
+                'type': 'continuous'},
+            'bootstrap': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[True, False]],
+                'type': 'categorical'}
         },
         'isolation_forest_class': {
-            'max_samples': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.05, 0.99]},
-            'max_features': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.05, 0.99]},
-            'bootstrap': {'hyperopt-dist': hp.choice, 'sampling-scope': [[True, False]]}
+            'max_samples': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.05, 0.99],
+                'type': 'continuous'},
+            'max_features': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.05, 0.99],
+                'type': 'continuous'},
+            'bootstrap': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[True, False]],
+                'type': 'categorical'}
         },
         'rfe_lin_reg': {
-            'n_features_to_select': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.5, 0.9]},
-            'step': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.1, 0.2]}
+            'n_features_to_select': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.5, 0.9],
+                'type': 'continuous'},
+            'step': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.1, 0.2],
+                'type': 'continuous'}
         },
         'rfe_non_lin_reg': {
-            'n_features_to_select': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.5, 0.9]},
-            'step': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.1, 0.2]}
+            'n_features_to_select': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.5, 0.9],
+                'type': 'continuous'},
+            'step': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.1, 0.2],
+                'type': 'continuous'}
         },
         'poly_features': {
-            'degree': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 5]},
-            'interaction_only': {'hyperopt-dist': hp.choice, 'sampling-scope': [[True, False]]}
+            'degree': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [2, 5],
+                'type': 'discrete'},
+            'interaction_only': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[True, False]],
+                'type': 'categorical'}
         },
         'polyfit': {
-            'degree': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 6]}
+            'degree': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 6],
+                'type': 'discrete'}
         },
         'lagged': {
-            'window_size': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [5, 500]}
+            'window_size': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [5, 500],
+                'type': 'discrete'}
         },
         'sparse_lagged': {
-            'window_size': (hp.uniformint, [5, 500]),
-            'n_components': (hp.uniform, [0, 0.5]),
-            'use_svd': (hp.choice, [[True, False]])
+            'window_size': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [5, 500],
+                'type': 'discrete'},
+            'n_components': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0, 0.5],
+                'type': 'continuous'},
+            'use_svd': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[True, False]],
+                'type': 'categorical'}
         },
         'smoothing': {
-            'window_size': (hp.uniformint, [2, 20])
+            'window_size': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [2, 20],
+                'type': 'discrete'}
         },
         'gaussian_filter': {
-            'sigma': (hp.uniform, [1, 5])
+            'sigma': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [1, 5],
+                'type': 'continuous'}
         },
         'diff_filter': {
-            'poly_degree': (hp.uniformint, [1, 5]),
-            'order': (hp.uniform, [1, 3]),
-            'window_size': (hp.uniform, [3, 20])
+            'poly_degree': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 5],
+                'type': 'discrete'},
+            'order': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [1, 3],
+                'type': 'continuous'},
+            'window_size': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [3, 20],
+                'type': 'continuous'}
         },
         'cut': {
-            'cut_part': (hp.uniform, [0, 0.9])
+            'cut_part': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0, 0.9],
+                'type': 'continuous'}
         },
         'lgbm': {
-            'class_weight': {'hyperopt-dist': hp.choice, 'sampling-scope': [[None, 'balanced']]},
-            'num_leaves': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 256]},
-            'learning_rate': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(0.01), np.log(0.2)]},
-            'colsample_bytree': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.4, 1]},
-            'subsample': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.4, 1]},
-            'reg_alpha': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(1e-8), np.log(10)]},
-            'reg_lambda': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(1e-8), np.log(10)]}
+            'class_weight': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[None, 'balanced']],
+                'type': 'categorical'},
+            'num_leaves': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [2, 256],
+                'type': 'discrete'},
+            'learning_rate': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [0.01, 0.2],
+                'type': 'continuous'},
+            'colsample_bytree': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.4, 1],
+                'type': 'continuous'},
+            'subsample': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.4, 1],
+                'type': 'continuous'},
+            'reg_alpha': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [1e-8, 10],
+                'type': 'continuous'},
+            'reg_lambda': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [1e-8, 10],
+                'type': 'continuous'}
         },
         'lgbmreg': {
-            'num_leaves': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 256]},
-            'learning_rate': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(0.01), np.log(0.2)]},
-            'colsample_bytree': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.4, 1]},
-            'subsample': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.4, 1]},
-            'reg_alpha': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(1e-8), np.log(10)]},
-            'reg_lambda': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(1e-8), np.log(10)]}
+            'num_leaves': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [2, 256],
+                'type': 'discrete'},
+            'learning_rate': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [0.01, 0.2],
+                'type': 'continuous'},
+            'colsample_bytree': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.4, 1],
+                'type': 'continuous'},
+            'subsample': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.4, 1],
+                'type': 'continuous'},
+            'reg_alpha': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [1e-8, 10],
+                'type': 'continuous'},
+            'reg_lambda': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [1e-8, 10],
+                'type': 'continuous'}
         },
         'catboost': {
-            'max_depth': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 11]},
-            'learning_rate': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(0.01), np.log(0.2)]},
-            'min_data_in_leaf': {'hyperopt-dist': hp.qloguniform, 'sampling-scope': [0, 6, 1]},
-            'border_count': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 255]},
-            'l2_leaf_reg': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(1e-8), np.log(10)]},
-            'loss_function': {'hyperopt-dist': hp.choice, 'sampling-scope': [['Logloss', 'CrossEntropy']]}
+            'max_depth': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 11],
+                'type': 'discrete'},
+            'learning_rate': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [0.01, 0.2],
+                'type': 'continuous'},
+            'min_data_in_leaf': {
+                'hyperopt-dist': partial(hp.qloguniform, q=1),
+                'sampling-scope': [0, 6],
+                'type': 'discrete'},
+            'border_count': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [2, 255],
+                'type': 'discrete'},
+            'l2_leaf_reg': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [1e-8, 10],
+                'type': 'continuous'}
         },
         'catboostreg': {
-            'max_depth': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [1, 11]},
-            'learning_rate': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(0.01), np.log(0.2)]},
-            'min_data_in_leaf': {'hyperopt-dist': hp.qloguniform, 'sampling-scope': [0, 6, 1]},
-            'border_count': {'hyperopt-dist': hp.uniformint, 'sampling-scope': [2, 255]},
-            'l2_leaf_reg': {'hyperopt-dist': hp.loguniform, 'sampling-scope': [np.log(1e-8), np.log(10)]}
+            'max_depth': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [1, 11],
+                'type': 'discrete'},
+            'learning_rate': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [0.01, 0.2],
+                'type': 'continuous'},
+            'min_data_in_leaf': {
+                'hyperopt-dist': partial(hp.qloguniform, q=1),
+                'sampling-scope': [0, 6],
+                'type': 'discrete'},
+            'border_count': {
+                'hyperopt-dist': hp.uniformint,
+                'sampling-scope': [2, 255],
+                'type': 'discrete'},
+            'l2_leaf_reg': {
+                'hyperopt-dist': hp.loguniform,
+                'sampling-scope': [1e-8, 10],
+                'type': 'continuous'}
         },
         'resample': {
-            'balance': {'hyperopt-dist': hp.choice, 'sampling-scope': [['expand_minority', 'reduce_majority']]},
-            'replace': {'hyperopt-dist': hp.choice, 'sampling-scope': [[True, False]]},
-            'balance_ratio': {'hyperopt-dist': hp.uniform, 'sampling-scope': [0.3, 1]}
+            'balance': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [['expand_minority', 'reduce_majority']],
+                'type': 'categorical'},
+            'replace': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[True, False]],
+                'type': 'categorical'},
+            'balance_ratio': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.3, 1],
+                'type': 'continuous'}
         },
         'lda': {
-            'solver': (hp.choice, [['svd', 'lsqr', 'eigen']]),
-            'shrinkage': (hp.uniform, [0.1, 0.9])
+            'solver': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [['svd', 'lsqr', 'eigen']],
+                'type': 'categorical'},
+            'shrinkage': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.1, 0.9],
+                'type': 'continuous'}
+        },
+        'ts_naive_average': {
+            'part_for_averaging': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.1, 1],
+                'type': 'continuous'}
+        },
+        'locf': {
+            'part_for_repeat': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.01, 0.5],
+                'type': 'continuous'}
         },
-        'ts_naive_average': {'part_for_averaging': (hp.uniform, [0.1, 1])},
-        'locf': {'part_for_repeat': (hp.uniform, [0.01, 0.5])},
         'word2vec_pretrained': {
-            'model_name': (hp.choice, [['glove-twitter-25', 'glove-twitter-50',
-                                        'glove-wiki-gigaword-100', 'word2vec-ruscorpora-300']])
+            'model_name': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [['glove-twitter-25', 'glove-twitter-50',
+                                    'glove-wiki-gigaword-100', 'word2vec-ruscorpora-300']],
+                'type': 'categorical'}
         },
         'tfidf': {
-            'ngram_range': (hp.choice, [[(1, 1), (1, 2), (1, 3)]]),
-            'min_df': (hp.uniform, [0.0001, 0.1]),
-            'max_df': (hp.uniform, [0.9, 0.99])
-        }, **industrial_search_space
+            'ngram_range': {
+                'hyperopt-dist': hp.choice,
+                'sampling-scope': [[(1, 1), (1, 2), (1, 3)]],
+                'type': 'categorical'},
+            'min_df': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.0001, 0.1],
+                'type': 'continuous'},
+            'max_df': {
+                'hyperopt-dist': hp.uniform,
+                'sampling-scope': [0.9, 0.99],
+                'type': 'continuous'}
+        },
     }
+    for key in industrial_search_space:
+        parameters_per_operation[key] = industrial_search_space[key]
 
     if self.custom_search_space is not None:
         for operation in self.custom_search_space.keys():
diff --git a/requirements.txt b/requirements.txt
index 6b2206498..e61a7f673 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,6 +23,7 @@ torch==2.0.0
 torchmetrics==0.11.4
 torchvision==0.15.1
 tensorboard>=2.12.0
+statsforecast==1.5.0
 
 chardet
 tqdm
\ No newline at end of file
diff --git a/tests/integration/repository/test_repo.py b/tests/integration/repository/test_repo.py
index 942f8f1f0..496a1c469 100644
--- a/tests/integration/repository/test_repo.py
+++ b/tests/integration/repository/test_repo.py
@@ -96,4 +96,4 @@ def test_tuner_fedot_uni_series():
 
     pipeline.print_structure()
     pipeline.fit(train_data)
-    pipeline.predict(test_data)
+    pipeline.predict(test_data)
\ No newline at end of file
diff --git a/tests/unit/models/test_ssa.py b/tests/unit/models/test_ssa.py
new file mode 100644
index 000000000..494863aa6
--- /dev/null
+++ b/tests/unit/models/test_ssa.py
@@ -0,0 +1,29 @@
+import numpy as np
+from fedot.core.data.data import InputData
+from fedot.core.data.data_split import train_test_data_setup
+
+from fedot.core.pipelines.pipeline_builder import PipelineBuilder
+from fedot.core.repository.dataset_types import DataTypesEnum
+from fedot.core.repository.tasks import TaskTypesEnum, Task, TsForecastingParams
+
+from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels
+
+
+def test_ssa():
+    time_series = np.random.random(30)
+    task = Task(TaskTypesEnum.ts_forecasting,
+                TsForecastingParams(forecast_length=1))
+    train_input = InputData(idx=np.arange(time_series.shape[0]),
+                            features=time_series,
+                            target=time_series,
+                            task=task,
+                            data_type=DataTypesEnum.ts)
+    train_data, test_data = train_test_data_setup(train_input)
+
+    with IndustrialModels():
+        pipeline = PipelineBuilder().add_node('data_driven_basis_for_forecasting',
+                                              params={'window_size': int(len(train_data.features) * 0.35)}
+                                              ).build()
+        pipeline.fit(train_data)
+        ssa_predict = np.ravel(pipeline.predict(test_data).predict)
+    assert (ssa_predict - test_data.target) < train_data.features.std()