  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 Similarity Assessment (MTASA). MTASA is
built upon a hybrid methodology designed to optimize time series alignment,
complemented by a multiprocessing engine that enhances the utilization of
computational resources. This integrated approach comprises four key
components, each addressing essential aspects of time series similarity
assessment, thereby offering a comprehensive framework for analysis. MTASA is
implemented as an open-source Python library with a user-friendly interface,
making it accessible to researchers and practitioners. To evaluate the
effectiveness of MTASA, we conducted an empirical study focused on assessing
agroecosystem similarity 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 to 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.
