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

Authors:

Maximilian Christ,

Julius Neuffer,

Andreas W. Kempa-LiehrAuthors Info & Claims

Volume 307, Issue C

Pages 72 - 77

https://doi.org/10.1016/j.neucom.2018.03.067

Published: 13 September 2018 Publication History

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.

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cover image Neurocomputing

Neurocomputing Volume 307, Issue C

Sep 2018

227 pages

ISSN:0925-2312

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 13 September 2018

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Cited By

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Schmidl SNaumann FPapenbrock T(2024)AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series DataProceedings of the VLDB Endowment10.14778/3681954.368197817:11(2987-3002)Online publication date: 1-Jul-2024
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Liu JWaz MChung JMoslehpour KLerner DBelding EGopalakrishnan VWang JAyyub Qazi ITyson G(2024)Estimating Video Quality Using Coarse-Grained Features: Insights and Limitations from Gaussian Mixture ModelsProceedings of the 20th International Conference on emerging Networking EXperiments and Technologies10.1145/3680121.3697811(10-16)Online publication date: 9-Dec-2024
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