Advancing multivariate time series similarity assessment: an integrated computational approach

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dc.creatorTonle, Franck Bruno Noumbo
dc.creatorTonnang, Henri E. Z.
dc.creatorNdadji, Milliam M. Z.
dc.creatorTchoupé Tchendji, Maurice
dc.creatorNzeukou, Armand
dc.creatorSenagi, Kennedy
dc.creatorNiassy, Saliou
dc.date2025
dc.date2025-07-29T09:53:15Z
dc.date2025-07-29T09:53:15Z
dc.date.accessioned2026-06-27T16:08:06Z
dc.descriptionData mining, particularly multivariate time series data analysis, is crucial 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 necessitating efficient and comprehensive analytical frameworks. A novel integrated computational approach, Multivariate Time series Alignment and Similarity Assessment (MTASA) is proposed to address these challenges. MTASA is built upon a hybrid methodology designed to optimise time series alignment, complemented by a multiprocessing engine that enhances the utilisation of computational resources. This integrated approach comprises four key components, each addressing essential aspects of time series similarity assessment, offering a comprehensive framework for analysis. To evaluate the effectiveness of MTASA, we conducted an empirical study focused on assessing agroecological similarity, a key aspect of climate smart agriculture, using real-world environmental data. The results from this study highlight MTASA’s superiority, achieving approximately 1.5 times greater accuracy and twice the speed compared with existing state-of-the-art integrated frameworks for multivariate time series similarity assessment. It is hoped that MTASA will significantly enhance the efficiency and accessibility of multivariate time series analysis, benefitting researchers and practitioners across various domains. Its capabilities in handling large datasets, addressing temporal misalignments, and delivering accurate results make MTASA a valuable tool for deriving insights and aiding decision-making processes in complex systems.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/175842
dc.identifier.urihttp://hdl.handle.net/123456789/123476
dc.languageen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.rightsOpen Access
dc.sourceTonle, F.B.N..; Tonnang, H.E.Z.; Ndadji, M.M.Z.; Tchoupé Tchendji, M.; Nzeukou, A.; Senagi, K.; Niassy, S. (2025) Advancing multivariate time series similarity assessment: an integrated computational approach. IEEE Access 13: 114639. ISSN: 2169-3536
dc.subjectpest control
dc.subjectartificial intelligence
dc.subjecttime series analysis
dc.subjectspecies diversity-similarity index
dc.titleAdvancing multivariate time series similarity assessment: an integrated computational approach
dc.typeJournal Article

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Instituto Internacional de Agricultura Tropical (IITA)