Advancing Multivariate Time Series Similarity Assessment: An Integrated Computational Approach
Publication Type
Journal Article
Journal Name
IEEE Access
Publication Date
1-1-2025
Abstract
Data 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.
Keywords
Computational approach, data mining, multiprocessing, multivariate time series, similarity assessment
Recommended Citation
Tonle, F., Tonnang, H., Ndadji, M., Tchendji, M., Nzeukou, A., Senagi, K., & Niassy, S. (2025). Advancing Multivariate Time Series Similarity Assessment: An Integrated Computational Approach. IEEE Access https://doi.org/10.1109/ACCESS.2025.3584712