TSSEARCH: Time Series Subsequence Search Libraryby Duarte Folgado, Marília Barandas, Margarida Antunes, Maria Lua Nunes, Hui Liu, Yale Hartmann, Tanja Schultz, Hugo Gamboa
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.
Reference:
TSSEARCH: Time Series Subsequence Search Library (Duarte Folgado, Marília Barandas, Margarida Antunes, Maria Lua Nunes, Hui Liu, Yale Hartmann, Tanja Schultz, Hugo Gamboa), In SoftwareX, volume 18, 2022.
Bibtex Entry:
@article{folgado2022tssearch,
title = {{TSSEARCH}: Time Series Subsequence Search Library},
author = {Folgado, Duarte and Barandas, Mar{\'\i}lia and Antunes, Margarida and Nunes, Maria Lua and Liu, Hui and Hartmann, Yale and Schultz, Tanja and Gamboa, Hugo},
journal = {SoftwareX},
volume = {18},
pages = {101049},
year = {2022},
issn = {2352-7110},
doi = {https://doi.org/10.1016/j.softx.2022.101049},
url = {https://www.csl.uni-bremen.de/cms/images/documents/publications/TSSEARCH_SoftwareX2022.pdf},
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.}
}