tsflex
Flexible time series feature extraction & processing (by predict-idlab)
tsfresh
Automatic extraction of relevant features from time series: (by blue-yonder)
tsflex | tsfresh | |
---|---|---|
3 | 4 | |
361 | 8,087 | |
1.4% | 0.5% | |
6.3 | 5.4 | |
8 days ago | 9 days ago | |
Python | Jupyter Notebook | |
MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tsflex
Posts with mentions or reviews of tsflex.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-11-12.
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[P] tsflex: flexible and efficient feature extraction for time series
tsflex its core functionality is strided-window feature extraction. This toolkit focusses on being flexible (e.g., few assumptions about sequence data, integration with other packages) and efficient (in both time & memory consumption -> see benchmark).
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Request - a good book for anomaly detection on time series
If you plan to transform your time series data in feature vectors, I would suggest to use tsflex (a rather new package) instead of TSfresh. tsflex is imo more convenient to use & multitudes more efficient for window-stride feature extraction.
tsfresh
Posts with mentions or reviews of tsfresh.
We have used some of these posts to build our list of alternatives
and similar projects.
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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
What are some alternatives?
When comparing tsflex and tsfresh you can also consider the following projects:
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
TimeSynth - A Multipurpose Library for Synthetic Time Series Generation in Python