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Application and assessment of machine learning models for weather prediction using LSTM neural networks.
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Table of Contents
This project gives an overview on how to develop a dense and deep neural network for making a time series prediction. Python’s development environment Jupyter, extended with the TensorFlow package and deep-learning application Keras is used. The main part shows an applied example of time series prediction with weather data. For this work, a deep recurrent neural network with Long Short-Term Memory cells is used to conduct the time series prediction. The results and evaluation of the work show that a weather prediction with deep neural networks can be successful for a short time period.
- Python (Version 3.7)
- packages can be installed using the requirements.txt file
- Jupyter Notebook
Please refer to the docs for a detailed description of the algorithm
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
Bojan Lukic - Website
Project Link: https://github.com/Bojan-Lukic/lstm-multivariate-time-series-prediction
- Aurélien Géron with Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
- Jason Brownlee with Time Series Prediction with LSTM Networks