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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

Xin RWang JChen PZhao Z(2025)Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A ReviewACM Computing Surveys10.1145/370174057:5(1-37)Online publication date: 9-Jan-2025
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Singh SChaturvedi A(2025)A Cooperative Game Theory-Based Feature Selection for Efficient Hand Grasp Classification Using Minimal Number of sEMG SignalsACM Transactions on Computing for Healthcare10.1145/37001436:1(1-22)Online publication date: 8-Jan-2025
https://dl.acm.org/doi/10.1145/3700143
Valentín-Coronado LMartínez-Manuel REsquivel-Hernández JMartínez-Guerrero MLaRochelle S(2025)Bending classification from interference signals of a fiber optic sensor using shallow learning and convolutional neural networksPattern Recognition Letters10.1016/j.patrec.2024.06.029186:C(354-360)Online publication date: 30-Jan-2025
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Wu XWu XZhang DZhang MGuo CYang BJensen C(2024)Fully Automated Correlated Time Series Forecasting in MinutesProceedings of the VLDB Endowment10.14778/3705829.370583518:2(144-157)Online publication date: 1-Oct-2024
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Falcetta ACristofaro GEpifani LRoveri M(2024)VETT: VectorDB-Enabled Transfer-Learning for Time-Series ForecastingProceedings of the 4th International Conference on AI-ML Systems10.1145/3703412.3704452(1-9)Online publication date: 8-Oct-2024
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