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Advancing multivariate time series similarity assessment: an integrated computational approach
March 19, 2024, 4:41 a.m. | Franck Tonle, Henri Tonnang, Milliam Ndadji, Maurice Tchendji, Armand Nzeukou, Kennedy Senagi, Saliou Niassy
cs.LG updates on arXiv.org arxiv.org
Abstract: Data mining, particularly the analysis of multivariate time series data, plays a crucial role in extracting insights from complex systems and supporting informed decision-making across diverse domains. However, assessing the similarity of multivariate time series data presents several challenges, including dealing with large datasets, addressing temporal misalignments, and the need for efficient and comprehensive analytical frameworks. To address all these challenges, we propose a novel integrated computational approach known as Multivariate Time series Alignment and …
abstract analysis arxiv assessment challenges complex systems computational cs.lg data data mining datasets decision diverse domains however insights large datasets making mining multivariate role series systems time series type
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