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Autoregressive models on wind gust forecasts

This is the repository for the bachelor thesis by Maik van Do. All models are free to downloaded and able to perform time series forecasting for single time series with high temporal resolutions. All models are build on classical time series analysis instruments such as $AR(p)$.

Thesis abstract

This thesis focus on the development of statistical-based machine learning approaches for autoregressive models specifically tailored for real time and offline short-term wind gust forecasts. Furthermore, applicable procedures for time series analysis models, which are capable of handling generalized Gaussian distributed real-time data were developed. The developed models exhibited high binary classification rates with $AUC$ values approaching approximately $0.9$. Moreover, these models can be efficiently deployed on small-scale computing platforms for prototyping purposes.

AR(3)Illustration

License

Copyright (c) <2023> 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.

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