Review of Multivariate Time Series Clustering Algorithms

Multivariate time series (MTS) data, serving as a crucial basis for intelligent technologies across numerous domains, record the state changes of multiple variables in systems over time. Clustering technique, as a core tool in data mining, can partition data into different clusters based on structural similarity, thereby uncovering the structure and internal relationships within data to discover systemic development patterns and variable correlations. Faced with the challenges such as the complexity of multivariate time series data structures, the interconnectivity between variables, and data high-dimensionality, a substantial amount of research has been conducted internationally. This paper provides an overview of clustering analysis algorithms for multivariate time series data scenarios. Initially, based on classification standards such as feature extraction methods, similarity measurement algorithms, and clustering partition frameworks, this paper conducts a comparative analysis of existing multivariate time series clustering algorithms. For each category of detection technology, a detailed summary is provided, covering algorithm principles, representative methods, advantages and disadvantages, and the problems they address. Further discussion includes common evaluation standards and publicly available datasets related to multivariate time series clustering. Lastly, from the perspective of the unique structure of multivariate temporal data, this paper outlines several challenging issues and future research directions.