Folgado, DuarteBarandas, MaríliaAntunes, MargaridaNunes, Maria LuaLiu, HuiHartmann, YaleSchultz, TanjaGamboa, Hugo2022-08-262022-08-262022-062352-7110PURE: 45154650PURE UUID: 6c4ef2ee-625c-416f-9452-e50eab58f806WOS: 000786596600001Scopus: 85127238013ORCID: /0000-0002-4022-7424/work/117952332http://hdl.handle.net/10362/143322Subsequence 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.5550399engTime seriesSubsequence searchDistancesSimilarity measurementsQuery-based searchSegmentationPython packagejournal article10.1016/j.softx.2022.101049