Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, Porto, Portugal; LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), NOVA School of Science and Technology (Campus de Caparica), 2829-516 Caparica, Portugal; Corresponding author at: Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, Porto, Portugal.
Marília Barandas
Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, Porto, Portugal; LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), NOVA School of Science and Technology (Campus de Caparica), 2829-516 Caparica, Portugal
Margarida Antunes
Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, Porto, Portugal
Maria Lua Nunes
Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, Porto, Portugal
Hui Liu
Cognitive Systems Lab, University of Bremen, Bremen, Germany
Yale Hartmann
Cognitive Systems Lab, University of Bremen, Bremen, Germany
Tanja Schultz
Cognitive Systems Lab, University of Bremen, Bremen, Germany
Hugo Gamboa
Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, Porto, Portugal; LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), NOVA School of Science and Technology (Campus de Caparica), 2829-516 Caparica, Portugal
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.
