Benchmarks of AutoML Frameworks for time series forecasting, anomaly detection and classification.
Primary Python version: 3.10.14
A Comparative Analysis of Automated Machine Learning Libraries for Electricity Price Forecasting (2024)
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To run this code, you will need to install CUDA for TensorFlow and PyTorch.
- CUDA compatibilities for TensorFlow are listed here.
- CUDA compatibilities for PyTorch are listed here
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Before running the code, datasets and repositories must be downloaded
After downloading repositories and datasets, you can run experiments with the following:
python run.pyRemoved. To be redrafted.
After installation and the download of repositories and datasets, you can run functional tests with:
Please feel free to get in touch at [email protected]
Christian O'Leary (2024) AutoML Python Benchmark.
@software{AutoML-Python-Benchmark,
author = {Christian O'Leary},
title = {AutoML Python Benchmark},
doi = {10.5281/zenodo.13133203},
howpublished = {\url{https://github.com/christian-oleary/AutoML-Python-Benchmark}},
year = {2024}
}