Tvarit_GmbH_challenge_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "54eiarWaOXVm"
   },
   "source": [
    "**Time Series Forcasting Challenge Data set - Tvarit GmbH**\n",
    "\n",
    "About the data set :  Contain engine with different ID operating houtrs & Fuel efficiency features for 100 days\n",
    "\n",
    "Aim : To develop a Machine Learning Model to forecast the next 15 days of engine operating hours.\n",
    "\n",
    "Method : LSTM has been used in this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# File Path\n",
    "file_path = '/..../Tvarit' #change dir to your project folder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "54eiarWaOXVm"
   },
   "source": [
    "**Step 1 - Import all the required Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "Gu01sVsSfVRD",
    "outputId": "e1e7de08-6bf0-44d0-bd8c-cc4c6c4d90e0"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import array\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler # for normalization\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from keras import Sequential, regularizers\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.layers import Dense, Input, GlobalMaxPooling1D, Conv1D, MaxPooling1D, Embedding, Conv2D\n",
    "from keras.optimizers import Adam,SGD\n",
    "from keras.layers import LSTM, Bidirectional, GlobalMaxPool1D, Dropout, Reshape, TimeDistributed, GRU\n",
    "from keras.models import load_model\n",
    "from keras.utils import to_categorical\n",
    "from keras.callbacks import EarlyStopping, LearningRateScheduler\n",
    "from keras.models import Model\n",
    "from pandas import Series\n",
    "from math import sqrt\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_yZPiZVMOiC6"
   },
   "source": [
    "**Step 2 - Read the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "colab_type": "code",
    "id": "eVI9nKnFf2Dh",
    "outputId": "6b1b1638-803f-4597-bce5-9c2c2fbfe7f4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>operating_hours</th>\n",
       "      <th>fuel_consumption(liter)</th>\n",
       "      <th>engine_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-19</td>\n",
       "      <td>3726</td>\n",
       "      <td>3404.8</td>\n",
       "      <td>8331744407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-20</td>\n",
       "      <td>3746</td>\n",
       "      <td>3865.6</td>\n",
       "      <td>8331744407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-21</td>\n",
       "      <td>3748</td>\n",
       "      <td>3891.2</td>\n",
       "      <td>8331744407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-22</td>\n",
       "      <td>3768</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>8331744407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-23</td>\n",
       "      <td>3776</td>\n",
       "      <td>3891.2</td>\n",
       "      <td>8331744407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  operating_hours  fuel_consumption(liter)   engine_id\n",
       "0  2017-12-19             3726                   3404.8  8331744407\n",
       "1  2017-12-20             3746                   3865.6  8331744407\n",
       "2  2017-12-21             3748                   3891.2  8331744407\n",
       "3  2017-12-22             3768                   3840.0  8331744407\n",
       "4  2017-12-23             3776                   3891.2  8331744407"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(file_path + '/' + 'challenge_data_20' + '.csv',sep=',')\n",
    "df.head()\n",
    "# Different engines ID with respective Operating hours available for every 100 rows #\n",
    "# print(df.loc[100:104])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U3jhJ8iX8W39"
   },
   "source": [
    "**Step 3 - Represent a unique number for every Engine ID & Basic Data Study**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 627
    },
    "colab_type": "code",
    "id": "EB7gmEXj8WTG",
    "outputId": "915963b2-d2bb-4e55-f64a-178f14e33a19"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         time  operating_hours  fuel_consumption(liter)   engine_id  engine_no\n",
      "0  2017-12-19             3726                   3404.8  8331744407          1\n",
      "1  2017-12-20             3746                   3865.6  8331744407          1\n",
      "2  2017-12-21             3748                   3891.2  8331744407          1\n",
      "3  2017-12-22             3768                   3840.0  8331744407          1\n",
      "4  2017-12-23             3776                   3891.2  8331744407          1\n",
      "\n",
      "Total Number of engines:  20\n",
      "\n",
      "Engine ID with respective days it run in highest order:\n",
      " 8331744407    101\n",
      "3413759796    101\n",
      "3308150982    101\n",
      "5687959787    100\n",
      "7133245688    100\n",
      "7940559597    100\n",
      "2654264148    100\n",
      "5246759857    100\n",
      "8481959000    100\n",
      "5452799100    100\n",
      "3850565523    100\n",
      "5452799200    100\n",
      "8711753003    100\n",
      "5452799300    100\n",
      "5452766296    100\n",
      "5452799400    100\n",
      "3643747759    100\n",
      "4569859790    100\n",
      "2159359786    100\n",
      "2582458400    100\n",
      "Name: engine_id, dtype: int64\n",
      "\n",
      "Total number of rows :  2003\n"
     ]
    }
   ],
   "source": [
    "# Give a unique number for corresponding engine ID # \n",
    "df['engine_no'] = (df.groupby(['engine_id']).cumcount()==0).astype(int)\n",
    "df['engine_no'] = df['engine_no'].cumsum()\n",
    "print(df.head())\n",
    "print()\n",
    "\n",
    "# Let us get some basic stats #\n",
    "print(\"Total Number of engines: \", len(df.engine_id.unique()))\n",
    "print()\n",
    "print('Engine ID with respective days it run in highest order:\\n', df['engine_id'].value_counts())\n",
    "print()\n",
    "print(\"Total number of rows : \", df.shape[0])\n",
    "#print(\"Unique engine Numbers : \", df.engine_id.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bB6Y2xEu2WUe"
   },
   "source": [
    "**Step 4 - Visualization of  how many days every engines operated**\n",
    "- From below graph it is clear that engine no 1,4,12 runs for 101 days\n",
    "- Rest all engines runs for 100 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 408
    },
    "colab_type": "code",
    "id": "Zf8JJfLe2Teb",
    "outputId": "bf31ad77-bcec-44da-9ca5-9d1265ac4a89"
   },
   "outputs": [
    {
     "data": {
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akiRJamSZliRJkhpZpiVJkqRGlmlJkiSpkWVakiRJamSZliRJkhpZpiVJkqRGlmlJkiSp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\nY1LWO+xPbT4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x='engine_no', data=df)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.axhline(y=100, linewidth = 0.5, color='k')\n",
    "plt.title(\"Each engine running days\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xQaiyrNKE1N1"
   },
   "source": [
    "\n",
    "**Step 5 - Calculate how many any hours each engine operate & Visualize it !**\n",
    "- Engine No 17 doesnot operated for whole 100 days.\n",
    "- Engine No 20 has highest Operating hours followed by Engine no 1\n",
    "- Engine No 5 has lowest operating hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 408
    },
    "colab_type": "code",
    "id": "h19ICF-s17ZQ",
    "outputId": "28f40d8c-beed-4e35-bf60-f44f1b6a3677"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OaagBpqMOa/ilaahjGmqA6ahjGmqA6ahjGmqA6aijaw1L4mJISZIkaalZKnO0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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split every engine data seperately # \n",
    "df_engine_index_1 = df.index[df.engine_id.ne(df.engine_id.shift(1)) == True].tolist()\n",
    "df_engine_index_2 = df.index[df.engine_id.ne(df.engine_id.shift(-1)) == True].tolist()\n",
    "\n",
    "# Save it in a Dictionay #\n",
    "df_engine_operatehours = {} # Only operating cost\n",
    "column_name = ['oper_hours', 'fuel', 'engine_no']\n",
    "\n",
    "for k, (i, j) in enumerate(zip(df_engine_index_1,df_engine_index_2)):\n",
    "    df_engine_operatehours['Engine_' + str(k + 1)] = df.loc[i : j, ['operating_hours', 'fuel_consumption(liter)']].reset_index(drop=True) \n",
    "    df_engine_operatehours['Engine_' + str(k + 1)]['Engine_No'] = int(k+1)\n",
    "    df_engine_operatehours['Engine_' + str(k + 1)].columns = column_name\n",
    "\n",
    "# Make a list, contains all 20 engine data as a Dataframe # \n",
    "df_list = [df_engine_operatehours[i] for i in df_engine_operatehours.keys()]\n",
    "\n",
    "# Every Engine operating hours calculation # \n",
    "list_run_hours = [df_list[i]['oper_hours'].iloc[-1] - df_list[i]['oper_hours'].iloc[0] for i in range(len(df_list))]\n",
    "\n",
    "# Plot the Operating Hours between 20 engines \n",
    "df_run_hours = pd.DataFrame({'Hours' : list_run_hours, 'engine_no' : range(1,21)})\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(x='engine_no', y= 'Hours', data=df_run_hours)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"No of hours Each engine runs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kSO2vJ8KgtYQ"
   },
   "source": [
    "**Step 6 - Visualize each engine Operating hours to check operating hours\n",
    "increasing trend**\n",
    "- In Plot, X axis denote NO OF DAYS and Y axis denote OPERATIGN HOURS\n",
    "\n",
    "**Step 6.1 - Abnormal Identification While plotting so marked RED**\n",
    "- Engine No 17 - Engine doesn't run.\n",
    "- Engine No : 18 -  Decrease & Increase.\n",
    "So normaly if an engine doesn't operate on a particular day the value must be constant from that point, but here the value decreases. \n",
    "- Engine No : 19 - Sudden Increase. On Analysis at day no, 79 & 80 values mentioned wrongly\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 667
    },
    "colab_type": "code",
    "id": "dvd0BIidXP8J",
    "outputId": "15d7117e-068e-40b6-f030-c730db40658a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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W5h6byyf1P6F87vKm4wgHtD5kPVZtpWnxpqajCDuR7uVu66WtbL60mUUnF/FZ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\n",
      "text/plain": [
       "<Figure size 792x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\n# Plot Eng_18 & Eng_19 graph seperately\\nplt.figure(figsize=(10.5,4))\\nplt.subplot(1,2,1)\\nplt.plot(df_list[17].oper_hours, 'b')\\nplt.title('Eng_18', fontsize = 10)\\nplt.subplot(1,2,2)\\nplt.plot(df_list[18].oper_hours, 'b')\\nplt.title('Eng_19', fontsize = 10)\\n\""
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot all 20 engines Operating hours # \n",
    "%matplotlib inline\n",
    "plt.figure()\n",
    "count = 0\n",
    "plt.figure(figsize=(11,10))\n",
    "for v in range(20):\n",
    "    plt.subplot(5,4, v+1)\n",
    "    if v == 16 or v == 17 or v == 18:\n",
    "      plt.plot(df_list[count].oper_hours, 'r')\n",
    "    else:\n",
    "      plt.plot(df_list[count].oper_hours, 'g')\n",
    "    plt.title('Eng_' + str(count+1), fontsize = 10)\n",
    "    plt.gca().axes.get_yaxis().set_visible(False)\n",
    "    plt.gca().axes.get_xaxis().set_visible(False)\n",
    "    count += 1\n",
    "plt.show()\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JvJXBjiZdb8X"
   },
   "source": [
    "**Step 7 - Analyzed and Corrected Engine No 19**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "colab_type": "code",
    "id": "dQHYMTmGgxXZ",
    "outputId": "16fd7087-fe7d-42bd-e921-a345819b98c0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:205: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Eng_19 - After Modification')"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1MNyDWcAOoA6wBkgcGX5Y1FZS+w+4e467Z7t7dt26dSsYXupbswY6doTLL4cj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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the values in Engine No - 19 to a proper values \n",
    "df_list_new = df_list\n",
    "df_list_new[18] = df_list_new[18].replace(to_replace=0, method='ffill')\n",
    "prob_ind = [i+1 for i in range(df_list_new[18].shape[0]-1) if df_list_new[18]['oper_hours'][i+1]- df_list_new[18]['oper_hours'][i] > 24]\n",
    "df_list_new[18]['oper_hours'].iloc[79:81] = df_list_new[18]['oper_hours'].iloc[prob_ind[0]:prob_ind[1]+ 1]/10\n",
    "# print(len(df_list_new))\n",
    "\n",
    "# Plot the corrected Engine data #\n",
    "plt.plot(df_list_new[18].oper_hours, 'b')\n",
    "plt.title('Eng_19 - After Modification', fontsize = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "38f3VsEeXSCA"
   },
   "source": [
    "**Step 8 - Final Check of the Engines Stats**\n",
    "- Print the engine operating hours in ascending order\n",
    "- under which operating hours value fall in a bin\n",
    "- Remove engine No 17 & 18 Finally, Total Engine = 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 590
    },
    "colab_type": "code",
    "id": "VWAQXIWFPJU5",
    "outputId": "7b9270c8-0c4b-446c-bb1d-7c83a52fdf41"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ascending order engine running hours :\n",
      " Hours  engine_no\n",
      "     0         17\n",
      "    28          5\n",
      "    99         18\n",
      "   169         14\n",
      "   208          4\n",
      "   213         13\n",
      "   239          9\n",
      "   296          7\n",
      "   323         15\n",
      "   459         16\n",
      "   495         19\n",
      "   550          8\n",
      "   722         11\n",
      "   804          3\n",
      "   842         12\n",
      "   887          6\n",
      "   961         10\n",
      "  1135          2\n",
      "  1614          1\n",
      "  2970         20\n",
      "\n",
      "Operating hours fall in a bin :\n",
      "(-2.971, 742.5]     13\n",
      "(742.5, 1485.0]      5\n",
      "(2227.5, 2970.0]     1\n",
      "(1485.0, 2227.5]     1\n",
      "Name: Hours, dtype: int64\n",
      "\n",
      "Total Engines after preprocessing : 18\n"
     ]
    }
   ],
   "source": [
    "# Final Engine Selection #\n",
    "\n",
    "print('Ascending order engine running hours :')\n",
    "print(df_run_hours.sort_values(by= ['Hours']).to_string(index=False))\n",
    "print()\n",
    "\n",
    "print('Operating hours fall in a bin :')\n",
    "print(df_run_hours['Hours'].value_counts(bins=4))\n",
    "print()\n",
    "\n",
    "df_new = [df_list_new[i]  for i in range(len(df_list_new)) if i not in [16, 17]]\n",
    "print('Total Engines after preprocessing :', len(df_new))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LsB2SBClVYYe"
   },
   "source": [
    "**Step 8.1 - Final plot w.r.t each engine operating hours after removing Engine 17 & 18**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 610
    },
    "colab_type": "code",
    "id": "dp6QeTId_aFu",
    "outputId": "defe2679-237b-44bc-a3b2-2d0091562bbf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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W5h6byyf1P6F87vKm4wgHtD5kPVZtpWnxpqajCDuR7uVu66WtbL60mUUnF/FZ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\nX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      "text/plain": [
       "<Figure size 792x720 with 18 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Final Plot\n",
    "plt.figure()\n",
    "count = 0\n",
    "plt.figure(figsize=(11,10))\n",
    "for v in range(18):\n",
    "    plt.subplot(5,4, v+1)\n",
    "    plt.plot(df_new[count].oper_hours, 'g')\n",
    "    plt.title('Eng_' + str(df_new[count].engine_no[0]),  fontsize = 10)\n",
    "    plt.gca().axes.get_yaxis().set_visible(False)\n",
    "    plt.gca().axes.get_xaxis().set_visible(False)\n",
    "    count += 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5UDhOpAsXFi0"
   },
   "source": [
    "**Step 9 - Important Preprocessing functions for training data preparation**\n",
    "- Difference function to calculate the operating hours difference between two days\n",
    "- Normalize the data\n",
    "- Convert the Univariate data into Supervised learning model (Sliding Window Approach), by taking the last 25 days as input and the next 15 days as output\n",
    "- All the engine data has been converted to supervised learning and saved in a list\n",
    "- For reference printed Engine No 1 Training data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 479
    },
    "colab_type": "code",
    "id": "g8T7GSmu7VNL",
    "outputId": "0d862da6-3717-4414-d2d7-c0a05e3ec60c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the past days to be considered as training data: 25\n",
      "Enter the no of days to be forecasted: 15\n",
      "Taken the last 25 days operating_hours data & predict the next 15 days\n",
      "\n",
      "converted every engine Univariate data into supervised Learning\n",
      "\n",
      "E.g Engine no 1 (X): \n",
      "    oper_hours(t-25)  oper_hours(t-24)  ...  oper_hours(t-2)  oper_hours(t-1)\n",
      "0         -0.820628         -0.982063  ...        -0.856502        -0.892377\n",
      "1         -0.982063         -0.820628  ...        -0.892377        -0.892377\n",
      "2         -0.820628         -0.928251  ...        -0.892377        -0.919283\n",
      "3         -0.928251         -0.910314  ...        -0.919283        -0.847534\n",
      "4         -0.910314         -0.910314  ...        -0.847534        -0.910314\n",
      "\n",
      "[5 rows x 25 columns]\n",
      "\n",
      "E.g Engine no 1 (Y): \n",
      "    oper_hours(t+1)  oper_hours(t+2)  ...  oper_hours(t+14)  oper_hours(t+15)\n",
      "0        -0.892377        -0.919283  ...         -1.000000         -1.000000\n",
      "1        -0.919283        -0.847534  ...         -1.000000         -1.000000\n",
      "2        -0.847534        -0.910314  ...         -1.000000         -1.000000\n",
      "3        -0.910314        -0.865471  ...         -1.000000          0.793722\n",
      "4        -0.865471        -0.955157  ...          0.793722         -0.856502\n",
      "\n",
      "[5 rows x 15 columns]\n"
     ]
    }
   ],
   "source": [
    "# Difference function #\n",
    "def difference(dataset, interval=1):\n",
    "\tdiff = list()\n",
    "\tfor i in range(interval, len(dataset)):\n",
    "\t\tvalue = dataset[i] - dataset[i - interval]\n",
    "\t\tdiff.append(value)\n",
    "\treturn Series(diff)\n",
    "\n",
    "# Normalize function #\n",
    "def normalize(dataset):\n",
    "  scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "  scaled_values = scaler.fit_transform(dataset)\n",
    "  scaled_values = scaled_values.reshape(len(scaled_values), 1)\n",
    "  return scaled_values, scaler\n",
    "\n",
    "# Converting into supervised Learning #\n",
    "def train_data(data, window = 25, pred_steps = 15,dropnan = True):\n",
    "    X, Y = list(), list()\n",
    "    for i in range(len(data)):\n",
    "      # print(i)\n",
    "      cols, names, y = list(), list(), list()\n",
    "      for j in range(window, 0, -1):\n",
    "          cols.append(data[i].shift(j))\n",
    "          names += [('%s(t-%d)' % (col,  j)) for col in ['oper_hours']] \n",
    "      agg = pd.concat(cols, axis=1).dropna().reset_index(drop=True)\n",
    "      agg.columns = names\n",
    "      # Target\n",
    "      for k in range(window, len(data[i]), 1):\n",
    "          y.append(list((data[i].shift(-k)[:pred_steps])))\n",
    "      #y = pd.DataFrame(np.array(y), columns = list(\"abcdefghijklmno\"))\n",
    "      y = pd.DataFrame(np.array(y), columns = ['oper_hours(t+' + str(i+1) + ')'for i in range(pred_steps)])\n",
    "      \n",
    "      # Combine\n",
    "      result = pd.concat([agg, y], axis=1).dropna()\n",
    "      X.append(result.loc[:, : 'oper_hours(t-1)'])\n",
    "      Y.append(result.loc[:, 'oper_hours(t+1)' : ])\n",
    "    return X, Y\n",
    "  \n",
    "# Training data prep function #\n",
    "def final_training_data(df_new, window, pred_steps):\n",
    "  # Step 1 - Take only Oper hours\n",
    "  df_step1 = [df_new[i].loc[:,'oper_hours'] for i in range(len(df_new))] # Taken only Oper hours\n",
    "  # Step 2 - difference calculation\n",
    "  df_step2 = [(difference(df_step1[i]).values).reshape(len(difference(df_step1[i]).values), 1) for i in range(len(df_step1))]\n",
    "  # Step 3 - Normalize the values and save the individual scaler in alist\n",
    "  df_step3, scaler = [], []\n",
    "  for i in range(len(df_step2)):\n",
    "    df_norm, norm_scaler = normalize(df_step2[i])\n",
    "    df_norm = pd.Series(df_norm.reshape(-1))\n",
    "    df_step3.append(df_norm)\n",
    "    scaler.append(norm_scaler)\n",
    "  # Step 4 - Convert to supervised\n",
    "  X, Y = train_data(df_step3, window, pred_steps)\n",
    "  return X, Y, scaler,df_step1, df_step2 \n",
    "\n",
    "# Call & get the training data #\n",
    "window = int(input('Enter the past days to be considered as training data: '))\n",
    "pred_steps = int(input('Enter the no of days to be forecasted: '))\n",
    "X, Y, scaler,df_step1,df_step2 = final_training_data(df_new, window, pred_steps)\n",
    "\n",
    "#'''\n",
    "# Print Engine No  data frame after converting the data into supervised learning #\n",
    "\n",
    "print(f'Taken the last {window} days operating_hours data & predict the next {pred_steps} days')\n",
    "print()\n",
    "print('converted every engine Univariate data into supervised Learning')\n",
    "print()\n",
    "print('E.g Engine no 1 (X): \\n',X[0].head()) \n",
    "print()\n",
    "print('E.g Engine no 1 (Y): \\n', Y[0].head())\n",
    "\n",
    "#'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OCTthiUJbXHf"
   },
   "source": [
    "**Step 10 - For Model Selection - LSTM Will be the nominal choise for Time series Forecasting**\n",
    "- More powerful in learning the pattern of the obervations from the start of the sequence\n",
    "- Recent research papers prove that LSTM produce a best result for Time series sequential Data analysis (e.g Text translations, NLP, Machine Translations etc)\n",
    "\n",
    "**Step 10.1 - Which models were initiated/used ?**\n",
    "- Based on user selection the Model and Engine no will be initiated for training\n",
    "- Statefull LSTM model, Vanilla LSTM(1 Layer), Stacked LSTM Model (2 Layer)\n",
    "- Model built for only one Engine Univariate data (engine no 1 -18)\n",
    "\n",
    "**Step 10.2 - Important Note**\n",
    "- After trying out different hyperparamters, the below hyper params were used for model training. \n",
    "- n_batches = no of batches, nb_epochs = no of epochs, n_neurons = no of neurons, relu optimizer,  \n",
    "- train_test_split = 80 : 20 ration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "cellView": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "colab_type": "code",
    "id": "LuEoXnVqFJ-o",
    "outputId": "e0998d48-8e02-4a94-ddf8-343bb4e3adab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Which Engine you would like to train : \n",
      " Select a number between 0 to 17 : 6\n",
      "Selection OK, Next Step\n",
      "\n",
      "Which model you would like to run : \n",
      " Option A) Statefull LSTM \n",
      " Option B) Vanilla LSTM \n",
      " Option C) Stacked LSTM \n",
      "A\n",
      "\n",
      "Select Train data percentage: 80\n",
      "12\n",
      "\n",
      "Training......\n",
      "Training Completed\n",
      "Time Taken 579.96s!\n"
     ]
    }
   ],
   "source": [
    "# Fit statefull LSTM\n",
    "def fit_lstm(X, Y, engine, test_split, n_batch, nb_epoch, n_neurons):\n",
    "  X_train, X_test = X[engine].values[0:-test_split], X[engine].values[-test_split:]\n",
    "  Y_train, Y_test = Y[engine].values[0:-test_split], Y[engine].values[-test_split:]\n",
    "\n",
    "  X_train = X_train.reshape(X_train.shape[0],X_train.shape[1],1)\n",
    "\n",
    "  model = Sequential()\n",
    "  model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X_train.shape[1], X_train.shape[2]), stateful=True))\n",
    "  model.add(Dense(Y_train.shape[1]))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  # fit network\n",
    "  for i in range(nb_epoch):\n",
    "    #print(i)\n",
    "    model.fit(X_train, Y_train, epochs=1, batch_size=n_batch, verbose=0,shuffle=False)\n",
    "    model.reset_states()\n",
    "  return model,X_test,Y_test\n",
    "\n",
    "# Vanilla LSTM  \n",
    "def fit_Vannila_LSTM(X, Y, engine,test_split, n_batch, nb_epoch, n_neurons_1):\n",
    "  X_train, X_test = X[engine].values[0:-test_split], X[engine].values[-test_split:]\n",
    "  Y_train, Y_test = Y[engine].values[0:-test_split], Y[engine].values[-test_split:]\n",
    "  X_train = X_train.reshape(X_train.shape[0],X_train.shape[1],1)\n",
    "  \n",
    "  model = Sequential()\n",
    "  # model.add(LSTM(n_neurons_1,  input_shape=(X_train.shape[1],1)))\n",
    "  model.add(LSTM(n_neurons_1, activation='relu',  input_shape=(X_train.shape[1],1)))\n",
    "  model.add(Dense(15))\n",
    "  model.compile(optimizer='adam', loss='mse')\n",
    "  H = model.fit(X_train, Y_train, epochs = nb_epoch, batch_size=n_batch, verbose=0, validation_data = (X_test.reshape(X_test.shape[0],X_test.shape[1],1), \n",
    "                                                                                                       Y_test), shuffle=False)\n",
    "  return model,H, X_test, Y_test \n",
    "\n",
    "# Stacked LSTM\n",
    "def fit_Stacked_lstm(X, Y, engine,test_split, n_batch, nb_epoch, n_neurons_1, n_neurons_2):\n",
    "  X_train, X_test = X[engine].values[0:-test_split], X[engine].values[-test_split:]\n",
    "  Y_train, Y_test = Y[engine].values[0:-test_split], Y[engine].values[-test_split:]\n",
    "  X_train = X_train.reshape(X_train.shape[0],X_train.shape[1],1)\n",
    "  \n",
    "  model = Sequential()\n",
    "  model.add(LSTM(n_neurons_1, activation='relu', return_sequences=True, input_shape=(X_train.shape[1],1)))\n",
    "  model.add(LSTM(n_neurons_2, activation='relu'))\n",
    "  model.add(Dense(15))\n",
    "  model.compile(optimizer='adam', loss='mse')\n",
    "  H = model.fit(X_train, Y_train, epochs = nb_epoch, batch_size=n_batch, verbose=0, validation_data = (X_test.reshape(X_test.shape[0],X_test.shape[1],1), \n",
    "                                                                                                       Y_test), shuffle=False)\n",
    "  return model,H, X_test, Y_test\n",
    "\n",
    "# Vanilla LSTM  \n",
    "def fit_Vannila_LSTM(X, Y, engine, test_split, n_batch, nb_epoch, n_neurons_1):\n",
    "  X_train, X_test = X[engine].values[0:-test_split], X[engine].values[-test_split:]\n",
    "  Y_train, Y_test = Y[engine].values[0:-test_split], Y[engine].values[-test_split:]\n",
    "  X_train = X_train.reshape(X_train.shape[0],X_train.shape[1],1)\n",
    "  \n",
    "  model = Sequential()\n",
    "  # model.add(LSTM(n_neurons_1,  input_shape=(X_train.shape[1],1)))\n",
    "  model.add(LSTM(n_neurons_1, activation='relu',  input_shape=(X_train.shape[1],1)))\n",
    "  model.add(Dense(15))\n",
    "  model.compile(optimizer='adam', loss='mse')\n",
    "  H = model.fit(X_train, Y_train, epochs = nb_epoch, batch_size=n_batch, verbose=0, validation_data = (X_test.reshape(X_test.shape[0],X_test.shape[1],1), \n",
    "                                                                                                       Y_test), shuffle=True)\n",
    "  return model,H, X_test, Y_test \n",
    "\n",
    "def model_selection(): \n",
    "  import time\n",
    "  Eng = int(input('Which Engine you would like to train : \\n Select a number between 0 to 17 : '))\n",
    "  if Eng not in [i for i in range(0, 18)]:\n",
    "    print('Enter Valid Engine ID')\n",
    "  else:\n",
    "    print('Selection OK, Next Step')\n",
    "  print()\n",
    "  Mod = input('Which model you would like to run : \\n Option A) Statefull LSTM \\n Option B) Vanilla LSTM \\n Option C) Stacked LSTM \\n')\n",
    "  print()\n",
    "  train_split = int(input('Select Train data percentage: '))\n",
    "  test_data = len(X[int(Eng)]) - round(len(X[int(Eng)])*(train_split/100))\n",
    "  print(test_data)\n",
    "  start = time.time()\n",
    "  print()\n",
    "  print('Training......')\n",
    "  \n",
    "  if Mod == 'A':\n",
    "    # Statefull LSTM model\n",
    "    model,X_test, Y_test = fit_lstm(X, Y, int(Eng), test_data, 1, 300, 3)\n",
    "  \n",
    "  elif Mod == 'B':\n",
    "    # fit Vannila LSTM\n",
    "    #model,H, X_test, Y_test = fit_Vannila_LSTM(X, Y, int(Eng),test_data, 8, 300, 3, validation_data = ())\n",
    "    model,H, X_test, Y_test = fit_Vannila_LSTM(X, Y, int(Eng),test_data, 8, 300, 3)\n",
    "  \n",
    "  elif Mod == 'C':\n",
    "  # fit stacked LSTM\n",
    "    model,H, X_test, Y_test = fit_Stacked_lstm(X, Y, int(Eng),test_data, 8, 300, 5, 3)\n",
    "\n",
    "  end = time.time()\n",
    "  print('Training Completed')\n",
    "  print('Time Taken {:.2f}s!'.format(end-start))\n",
    "  \n",
    "  # plot the model Loss details\n",
    "  # plt.figure()\n",
    "  # plt.plot(H.history['loss'])\n",
    "  # plt.plot(H.history['val_loss'])\n",
    "  # plt.title('model loss')\n",
    "  # plt.ylabel('loss')\n",
    "  # plt.xlabel('epoch')\n",
    "  # plt.legend(['train', 'test'], loc='upper left')\n",
    "  # plt.show()\n",
    " \n",
    "  return model, X_test, Y_test, Eng\n",
    "\n",
    "model, X_test, Y_test, engine_no = model_selection()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TDsluWrgZid0"
   },
   "source": [
    "**Step 11 - Model Forecasting and RMSE Score calculation**\n",
    "- Testing Data = 12 (20 %)\n",
    " \n",
    "**Step 11.1 Plot each predicted values in a plot over the actual value plot**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "HuFnoBKj8CFc",
    "outputId": "5be41d56-94d5-4949-fb64-844f6f5aed8e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RMSE_Score Values\n",
      "from day 48 to 72 the Actual VS Prdiction RMSE score =  2.3015656859173466\n",
      "from day 49 to 73 the Actual VS Prdiction RMSE score =  2.600271085462768\n",
      "from day 50 to 74 the Actual VS Prdiction RMSE score =  2.6534263909972524\n",
      "from day 51 to 75 the Actual VS Prdiction RMSE score =  2.829571198274649\n",
      "from day 52 to 76 the Actual VS Prdiction RMSE score =  2.7261200334740368\n",
      "from day 53 to 77 the Actual VS Prdiction RMSE score =  2.9356266349695743\n",
      "from day 54 to 78 the Actual VS Prdiction RMSE score =  2.460449892796526\n",
      "from day 55 to 79 the Actual VS Prdiction RMSE score =  2.4992899786980636\n",
      "from day 56 to 80 the Actual VS Prdiction RMSE score =  2.667649443460694\n",
      "from day 57 to 81 the Actual VS Prdiction RMSE score =  2.7879948350681367\n",
      "from day 58 to 82 the Actual VS Prdiction RMSE score =  2.8006121437543205\n",
      "from day 59 to 83 the Actual VS Prdiction RMSE score =  2.824741155415796\n",
      "\n",
      "R2_Score Values\n",
      "from day 48 to 72 the Actual VS Prdiction r2 score =  0.8825279877308061\n",
      "from day 49 to 73 the Actual VS Prdiction r2 score =  0.8526712002202123\n",
      "from day 50 to 74 the Actual VS Prdiction r2 score =  0.8719774436076322\n",
      "from day 51 to 75 the Actual VS Prdiction r2 score =  0.8965037077804364\n",
      "from day 52 to 76 the Actual VS Prdiction r2 score =  0.919204708721279\n",
      "from day 53 to 77 the Actual VS Prdiction r2 score =  0.9285966879699835\n",
      "from day 54 to 78 the Actual VS Prdiction r2 score =  0.956487730741549\n",
      "from day 55 to 79 the Actual VS Prdiction r2 score =  0.9575904846268972\n",
      "from day 56 to 80 the Actual VS Prdiction r2 score =  0.9562327916720661\n",
      "from day 57 to 81 the Actual VS Prdiction r2 score =  0.9593599033303322\n",
      "from day 58 to 82 the Actual VS Prdiction r2 score =  0.9630692800099938\n",
      "from day 59 to 83 the Actual VS Prdiction r2 score =  0.9650948480791413\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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aN7cC8TuX0cxg6tRwt3ZZCgrgiy/COFFHHlk5sbnMUJGEUSf6dyjwopnlqwK3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\n5xLiCcM551xCPGE455xLyP8HhkaAHThC72AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pv2YVkar4c3f2VGNMU9yg5HettVuO3MEYMwEYhFuftBBoBvwCeLhol3uBm4u2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\nmK8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 792x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def forecast_RMSE_calculator(model, X_test, Y_test, engine_no, pred_steps, window):\n",
    "  if len(X_test.shape) == 1:\n",
    "    X_test = X_test.reshape(1,X_test.shape[0],1)\n",
    "  else:\n",
    "    X_test = X_test.reshape(X_test.shape[0],X_test.shape[1],1)\n",
    "\n",
    "  # Forecast\n",
    "  forecasts = model.predict(X_test, batch_size=1)\n",
    "  final_forecasts = []\n",
    "  #print('model_forecasts :', len(forecasts))\n",
    "\n",
    "  # Find indices\n",
    "  indices = []\n",
    "  for i in range(len(forecasts)):\n",
    "    invert = array(forecasts[i]).reshape(1,len(forecasts[i]))\n",
    "    inv_scale = scaler[engine_no].inverse_transform(invert)\n",
    "    inv_scale = inv_scale[0, :]\n",
    "    final_forecasts.append(inv_scale)\n",
    "    # find index\n",
    "    index = len(X[engine_no]) - len(X_test) +  i  # len(X_test) or test_split\n",
    "    index = index + 25\n",
    "    indices.append(index)\n",
    "  # print(indices) \n",
    "  final_forecasts = np.array(final_forecasts)\n",
    "  \n",
    "  # print('Indices :', indices)\n",
    "  # print('final_forecasts :', final_forecasts)\n",
    "\n",
    "  # Finding actual difference\n",
    "  act = np.array([df_step2[engine_no][indices[i] : indices[i]+ pred_steps] for i in range(len(indices))])\n",
    "  act = act.reshape((X_test.shape[0],pred_steps))\n",
    "  #print('act :',act)\n",
    "\n",
    "  # Actual Values in Hours\n",
    "  original = []\n",
    "  for i in range(len(indices)):# 12\n",
    "    temp1 = []\n",
    "    for j in range(act.shape[1]):# 15\n",
    "      temp1.append(df_step1[engine_no][indices[i]+j] + act[i][j])\n",
    "    original.append(temp1)\n",
    "  original = np.array(original)\n",
    "\n",
    "  # predicted Value hours\n",
    "  predict = []\n",
    "  for i in range(len(indices)):# 12\n",
    "    temp2 = []\n",
    "    for j in range(final_forecasts.shape[1]):# 15\n",
    "      temp2.append(df_step1[engine_no][indices[i]+j] + final_forecasts[i][j])\n",
    "    predict.append(temp2)\n",
    "  predict = np.array(predict)\n",
    "\n",
    "  # print(original)\n",
    "  # print(predict.shape)\n",
    "\n",
    "  # RMSE calculation\n",
    "  print()\n",
    "  print('RMSE_Score Values')\n",
    "  for i in range(len(X_test)):\n",
    "    ACTUAL = original[i]\n",
    "    FORECAST = predict[i]\n",
    "    rmse = sqrt(mean_squared_error(ACTUAL, FORECAST))\n",
    "    print(f'from day {indices[i]-25} to {indices[i]-1} the Actual VS Prdiction RMSE score =  {rmse}')\n",
    "  \n",
    "  # R2 calculation :\n",
    "  print()\n",
    "  print('R2_Score Values')\n",
    "  for i in range(len(X_test)):\n",
    "    ACTUAL = original[i]\n",
    "    FORECAST = predict[i]\n",
    "    r2 = r2_score(ACTUAL, FORECAST)\n",
    "    print(f'from day {indices[i]-window} to {indices[i]-1} the Actual VS Prdiction r2 score =  {r2}')\n",
    "  \n",
    "  print()\n",
    "  plt.figure()\n",
    "  plt.plot(df_step1[engine_no], 'b', alpha=0.5)\n",
    "  plt.title(f'{len(X_test)} Prediction values plotted in single graph')\n",
    "  plt.xlabel('No of days')\n",
    "  plt.ylabel('Oper_Hours')\n",
    "  for i in range(len(predict)):\n",
    "    # x_axis = df_step1[engine][indices[i]+1 : indices[i]+ 1 +15]\n",
    "    x_axis = range(indices[i]+1, indices[i]+ 1 +15)\n",
    "    y_axis = predict[i]\n",
    "    # print(x_axis, y_axis)\n",
    "    plt.plot(x_axis, y_axis, 'r', linewidth=3)\n",
    "  plt.legend(['Overall', 'Prediction'], fontsize= 10)\n",
    "  \n",
    "  print()  \n",
    "  plt.figure()\n",
    "  plt.figure(figsize=(11,10))\n",
    "  for i in range(len(predict)):\n",
    "    plt.subplot(4,3, i+1)\n",
    "    X_axis = range(indices[i]-25 + 1, indices[i]+1)\n",
    "    Y_axis = df_step1[engine_no].iloc[X_axis[0] : X_axis[-1]+1]\n",
    "\n",
    "    x_axis = range(indices[i]+1, indices[i]+ 1 +15)\n",
    "    y_axis = predict[i]\n",
    "    plt.xlabel('No of days', fontsize=6)\n",
    "    plt.ylabel('Oper_hours prediction ', fontsize=6)\n",
    "    plt.ylabel('Oper_hours prediction ', fontsize=6)\n",
    "    plt.tick_params(axis='both',labelsize=5)\n",
    "    plt.plot(df_step1[engine_no], 'b', alpha = 0.5)\n",
    "    plt.plot(X_axis, Y_axis, 'g',linewidth = 3)\n",
    "    plt.plot(x_axis, y_axis, 'r', linewidth= 3)\n",
    "\n",
    "    # to remove the x axis label values\n",
    "    #plt.gca().axes.get_yaxis().set_visible(False)\n",
    "    #plt.gca().axes.get_xaxis().set_visible(False)\n",
    "    \n",
    "  plt.legend(['Overall', 'Testing', 'Prediction'], fontsize=8)\n",
    "  plt.show()\n",
    "\n",
    "  return original, predict\n",
    "\n",
    "Original, Prediction = forecast_RMSE_calculator(model, X_test, Y_test, engine_no, pred_steps = 15, window=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ad0XwkrfipWW"
   },
   "source": [
    "**Conclusion**\n",
    "- The above model and its results is just the first revision work for time series forecasting.\n",
    "- There is still a room for improvement in the model.\n",
    "- Even though the above models produce a good accuracy/prediction rate.\n",
    "- RMSE Score & R2 score metrics has been employed to check the performance.\n",
    "- As per the above results, Stacked LSTM & Statfull LSTM with 1 Layer Model Produces a good result when comparing with other two models (Detail Analysis has been shown in ppt attached)\n",
    "\n"
   ]
  }
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