Escudero-Arnanz, ÓscarG. Marques, AntonioSoguero-Ruiz, CristinaMora-Jiménez, InmaculadaRobles, Gregorio2023-10-092023-10-092023Óscar Escudero-Arnanz, Antonio G. Marques, Cristina Soguero-Ruiz, Inmaculada Mora-Jiménez, Gregorio Robles, dtwParallel: A Python package to efficiently compute dynamic time warping between time series, SoftwareX, Volume 22, 2023, 101364, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2023.1013642352-7110https://hdl.handle.net/10115/24752Work supported by the Spanish NSF (grants , PID2019-106623RB-C41/AEI/10.13039/501100011033 PID2019-105032GB-I00AEI/10.13039/501100011033, and PID2019-107768RA-I00/AEI/10.13039/501100011033), and the Community of Madrid, Spain (grants PEJ-2020-AI/TIC-18964, URJC-F661, and e-Madrid-CM-P2018/TCS-4307, co-financed by EU Structural Funds FSE and FEDER, Spain ).dtwParallel is a Python package that computes the Dynamic Time Warping (DTW) distance between a collection of (multivariate) time series (MTS). dtwParallel incorporates the main functionalities available in current DTW libraries and novel functionalities such as parallelization, computation of similarity (kernel-based) values, and consideration of data with different types of features (categorical, real-valued, . . . ). A low-floor, high-ceiling, and wide-walls software design principle has been adopted, envisioning uses in education, research, and industry. The source code and documentation of the package are available at https://github.com/oscarescuderoarnanz/dtwParallel.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/DTWMultivariate Time SeriesKernel-based similarityParallelizationPythoninfo:eu-repo/semantics/article10.1016/j.softx.2023.101364info:eu-repo/semantics/openAccess