Litcius/Paper detail

TSSEARCH: Time Series Subsequence Search Library

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

2022SoftwareX31 citationsDOIOpen Access PDF

Abstract

a b s t r a c t 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.

Topics & Concepts

SubsequenceComputer sciencePython (programming language)Longest common subsequence problemData miningSeries (stratigraphy)Nearest neighbor searchContext (archaeology)Time seriesMerge (version control)Information retrievalAlgorithmMachine learningProgramming languageMathematicsMathematical analysisBounded functionPaleontologyBiologyTime Series Analysis and ForecastingAnomaly Detection Techniques and ApplicationsData Visualization and Analytics
TSSEARCH: Time Series Subsequence Search Library | Litcius