tsfresh
Deep-Learning-Machine-Learning-Stock
| tsfresh | Deep-Learning-Machine-Learning-Stock | |
|---|---|---|
| 4 | 5 | |
| 8,096 | 792 | |
| 0.5% | - | |
| 5.4 | 10.0 | |
| 15 days ago | about 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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tsfresh
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For deep learning practitioners in industry, is the workflow always this annoying? [D]
This is definitely a good thing to try for time-series; you can automate your feature extraction too (eg using https://github.com/blue-yonder/tsfresh ).
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[D] Incorporating external data in LSTM models for sales forecasting in e-commerce
don't forget your feature engineering -> https://github.com/blue-yonder/tsfresh
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[R] Approach to identify clusters on a time series
Rather than the exact clustering algorithm, I think the main issue here is the feature extraction for the clustering. https://github.com/blue-yonder/tsfresh might be useful for that.
- Automatic time series feature extraction based on scalable hypothesis tests
Deep-Learning-Machine-Learning-Stock
What are some alternatives?
tsflex - Flexible time series feature extraction & processing
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