[TSSEARCH Codebank] TSSEARCH: Time Series Subsequence Search Library

Date:2025-05-23  Hits:

Impact Factor:3.4

DOI Number:10.1016/j.softx.2022.101049

Affiliation of Author(s):University of Bremen

Journal:SoftwareX

Place of Publication:Netherlands

Key Words:time series; subsequence search; distances; similarity measurements; query-based search; segmentation; Python package

Abstract:Subsequence search and distance measures are crucial tools in time series data mining. This paper presents our Python package entitled TSSEARCH, which provides a comprehensive set of methods for subsequence search and similarity measurement in time series. These methods are user-customizable for more flexibility and efficient integration into real deployment scenarios. TSSEARCH enables fast exploratory time series data analysis and was validated in the context of human activity recognition and indoor localization.

Note:https://tssearch.readthedocs.io/en/latest/

All the Authors:Duarte Folgado*, Marília Barandas, Margarida Antunes, Maria Lua Nunes, Hui Liu, Yale Hartmann, Tanja Schultz, Hugo Gamboa

Indexed by:Journal paper

Discipline:Engineering

Document Type:J

Volume:18

Page Number:101049

ISSN No.:2352-7110

Translation or Not:no

Date of Publication:2022-06-01

Included Journals:SCI

Publication links:https://www.sciencedirect.com/science/article/pii/S2352711022000425

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