Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)

TLDR

Time‑series feature engineering is time‑consuming because it requires many signal‑processing and analysis algorithms to extract meaningful features. tsfresh accelerates this process by combining 63 characterization methods that compute 794 features with automated hypothesis‑test‑based feature selection. The package implements standard APIs such as pandas and scikit‑learn, enabling both exploratory analysis and seamless integration into operational data‑science workflows. By identifying statistically significant features early, tsfresh closes feedback loops with domain experts and promotes the early development of domain‑specific features.

Abstract

Time series feature engineering is a time-consuming process because scientists and engineers have to consider the multifarious algorithms of signal processing and time series analysis for identifying and extracting meaningful features from time series. The Python package tsfresh (Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests) accelerates this process by combining 63 time series characterization methods, which by default compute a total of 794 time series features, with feature selection on basis automatically configured hypothesis tests. By identifying statistically significant time series characteristics in an early stage of the data science process, tsfresh closes feedback loops with domain experts and fosters the development of domain specific features early on. The package implements standard APIs of time series and machine learning libraries (e.g. pandas and scikit-learn) and is designed for both exploratory analyses as well as straightforward integration into operational data science applications.