blue-yonder/tsfresh

Health v2 · maintenance only

Adoption not part of the score

9299 stars · 1274 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

tsfresh is a Python package that automatically extracts hundreds of features from time series using algorithms from statistics, signal processing, and nonlinear dynamics. It includes a hypothesis-test-based filtering procedure to select only the features relevant for a given regression or classification task.

Use cases

• extract features from time series for machine learning
• automate feature engineering for sensor data
• classify time series data
• forecast with time series features
• filter irrelevant features from time series
• build scikit-learn pipelines for time series

When to choose

• you need many time series features computed automatically
• you want built-in relevance filtering via hypothesis tests
• you work in Python with pandas DataFrames and scikit-learn

When to avoid

• you need deep-learning-based time series representations
• your data is not time series or event-sequence shaped
• you need extremely low-latency feature computation without parallelization setup

Facets

library · maturity stable

machine-learning data-science etl data-science machine-learning time-series analytics python cross-platform time-series feature-extraction feature-selection hypothesis-testing scikit-learn

Member repositories

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markdown · JSON · MCP: product_card(name="blue-yonder/tsfresh")