Snorkel helps generate training data programmatically through weak supervision.
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tsfresh automatically extracts relevant features from time series data.
About TSFresh
TSFresh is an open-source Python package for automatic feature extraction from time series. It combines methods from statistics, time-series analysis, signal processing, and nonlinear dynamics with a feature selection step, and treats time series broadly to include sampled data and event sequences.
The package extracts hundreds of features, from basic characteristics such as number of peaks, average, or maximal value to more complex measures such as time reversal symmetry. The resulting features can be used to build statistical or machine learning models for regression or classification, and a hypothesis-testing-based filtering procedure evaluates feature importance to limit irrelevant features.
Key features
- Automatically extracts hundreds of time-series characteristics
- Calculates basic statistics like peaks, average, maximal values
- Calculates complex measures like time reversal symmetry statistic
- Filters features using hypothesis-testing-based selection procedure
- Supports use in regression and classification tasks
- Handles sampled data and event sequences as time series
Best for: Data scientists using Python who need time-series features for regression or classification models.
Good for
- Open-source Python package
- Extracts hundreds of features automatically
- Includes hypothesis-testing-based filtering to limit irrelevant features
TSFresh at a glance
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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TSFresh FAQ
What does TSFresh stand for?
Time Series Feature extraction based on scalable hypothesis tests.
What can the extracted features be used for?
They can be used to construct statistical or machine learning models for regression or classification tasks.
How does TSFresh address irrelevant features?
It includes a filtering procedure that evaluates each feature's importance for the task using hypothesis testing and a multiple test procedure.
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