TSSEARCH: Time Series Subsequence Search Library

Duarte Folgado, Marília Barandas, Margarida Antunes, Maria Lua Nunes, Hui Liu, Yale Hartmann, Tanja Schultz, Hugo Gamboa

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
127 Downloads (Pure)

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.

Original languageEnglish
Article number101049
Pages (from-to)1-5
Number of pages5
JournalSoftwareX
Volume18
DOIs
Publication statusPublished - Jun 2022

Keywords

  • Time series
  • Subsequence search
  • Distances
  • Similarity measurements
  • Query-based search
  • Segmentation
  • Python package

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