https://pypi.org/project/tsmd/ Recent updates to the Python Package Index for tsmd en https://pypi.org/project/tsmd/0.1.4/ The TSMD project brings together Motif Discovery methods for Time Series, aiming to compare their performance through well-defined research questions and to simplify their practical use. It provides both guidelines for selecting the most suitable methods based on the data, and accessible implementations of the most relevant approaches. valerio.guerrini@ens-paris-saclay.fr, thibaut.germain@ens-paris-saclay.fr, charles.truong@ens-paris-saclay.fr, laurent.oudre@ens-paris-saclay.fr, paul.boniol@inria.fr Thu, 28 Aug 2025 15:44:13 GMT https://pypi.org/project/tsmd/0.1.3/ The TSMD project brings together Motif Discovery methods for Time Series, aiming to compare their performance through well-defined research questions and to simplify their practical use. It provides both guidelines for selecting the most suitable methods based on the data, and accessible implementations of the most relevant approaches. valerio.guerrini@ens-paris-saclay.fr, thibaut.germain@ens-paris-saclay.fr, charles.truong@ens-paris-saclay.fr, laurent.oudre@ens-paris-saclay.fr, paul.boniol@inria.fr Mon, 12 May 2025 12:40:17 GMT https://pypi.org/project/tsmd/0.1.2/ The TSMD project brings together Motif Discovery methods for Time Series, aiming to compare their performance through well-defined research questions and to simplify their practical use. It provides both guidelines for selecting the most suitable methods based on the data, and accessible implementations of the most relevant approaches. valerio.guerrini@ens-paris-saclay.fr, thibaut.germain@ens-paris-saclay.fr, charles.truong@ens-paris-saclay.fr, laurent.oudre@ens-paris-saclay.fr, paul.boniol@inria.fr Mon, 28 Apr 2025 12:03:54 GMT https://pypi.org/project/tsmd/0.1.1/ The TSMD project brings together Motif Discovery methods for Time Series, aiming to compare their performance through well-defined research questions and to simplify their practical use. It provides both guidelines for selecting the most suitable methods based on the data, and accessible implementations of the most relevant approaches. valerio.guerrini@ens-paris-saclay.fr, thibaut.germain@ens-paris-saclay.fr, charles.truong@ens-paris-saclay.fr, laurent.oudre@ens-paris-saclay.fr, paul.boniol@inria.fr Mon, 28 Apr 2025 08:53:08 GMT https://pypi.org/project/tsmd/0.1.0/ The TSMD project brings together Motif Discovery methods for Time Series, aiming to compare their performance through well-defined research questions and to simplify their practical use. It provides both guidelines for selecting the most suitable methods based on the data, and accessible implementations of the most relevant approaches. valerio.guerrini@ens-paris-saclay.fr, thibaut.germain@ens-paris-saclay.fr, charles.truong@ens-paris-saclay.fr, laurent.oudre@ens-paris-saclay.fr, paul.boniol@inria.fr Fri, 25 Apr 2025 13:06:47 GMT