tsfresh is a Python library for automatic time series feature extraction and selection, grounded in the FRESH (FeatuRe Extraction based on Scalable Hypothesis tests) algorithm. It provides over 700 feature calculators spanning statistics, signal processing, and nonlinear dynamics, along with hypothesis-testing-based feature selection using Benjamini-Hochberg or Benjamini-Yekutieli FDR control. The library is designed for integration into scikit-learn pipelines via transformers like FeatureAugmenter and RelevantFeatureAugmenter, and supports parallel/distributed execution via multiprocessing, Dask, or Spark. While primarily a software library (application), it has strong research roots with academic publications and example notebooks demonstrating classification, regression, and forecasting tasks. The codebase is well-structured with clean abstractions for data input normalization (adapters), pluggable parallelization (distributors), and feature settings reconstruction, making it a robust tool for time series feature engineering in production ML workflows.