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LICENSE
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MIT License
Copyright (c) 2024 RiSchmi
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Datasource:
The usage of the meteorological data is regulated by the "Creative Commons BY 4.0" (CC BY 4.0) and covers the following data sets:
• Hourly station observations of 2 m air temperature and humidity for Germany, Version v24.03
• Hourly station observations of precipitation for Germany, Version v24.03
• Hourly station observations of pressure for Germany, Version v24.03
• Hourly station observations of solar incoming (total/diffuse) and longwave downward radiation for Germany, Version v24.03
• Hourly mean value from station observations of wind speed and wind direction for Germany, Version v24.03
Ackknowledgement: The architecture for multi-step forecast, applied for missing data imputation, has been altered from the tutorial by Brownlee (2020).
The original owner of the data and code used in this thesis retains ownership of the data and code.
Brownlee, J. (2020). Multi-step time series forecasting with machine learning for electricity usage.
https://machinelearningmastery.com/multi-step-time-series-forecasting-with-machine-learningmodels-for-household-electricity-consumption/