Sun, Hanlin, Jie, Wei ORCID: https://orcid.org/0000-0002-5392-0009, Chen, Yanping and Wang, Zhongmin (2025) A Comparison Study of Several Strategies in Multivariate Time Series Clustering Based on Graph Community Detection. Applied Intelligence. ISSN 0924-669X (In Press)

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Abstract

Time series data analysis, especially forecasting, classification, imputation and anomaly detection, has gain a lot of research attention in recent years due to its prevalence and wide application. Compared to classification, clustering is an unsupervised task and thus more applicable for analyzing massive time series without labels. One latest way is based on the idea of graph community detection: first transforming a time series set into a graph (or a network), in which a node represents a time series instance and an edge denotes that the two connected nodes (thus the represented time series) are more similar to each other; then, running a community detection algorithm on the graph to discover a community structure, that gives out a clustering result. Recently, there are several works based on the graph community detection idea to cluster multivariate time series. However, such works focus only on specific method in each step, and a performance comparison of combinations of methods in different steps is lack. This
paper outlines the process of graph based multivariate time clustering as four phases (referred as framework), namely representation learning, similarity computing, relation network construction and clustering, lists typical methods in each phase, and makes a comparison study of combinations of each phase methods (called strategies in this paper). Recent time series deep neural network models are introduced to the framework as time series representation learning methods as well. In addition, εkNN, an improvement of kNN by filtering out unnecessary low similarity connections is proposed. A great number of experiments are conducted on eight real word multivariate time series with various properties to verify the performance of different strategy combinations. The results suggest that proper deep neural network is a promising way for learning time series vector
representations to compute similarities, and strategies including εkNN for network construction, average for multi-layer network merging and Louvain for community detection are more effective from statistic perspective.

Item Type:	Article
Subjects:	Computing
Depositing User:	WEI JIE
Date Deposited:	10 Mar 2025 08:32
Last Modified:	10 Mar 2025 08:45
URI:	https://repository.uwl.ac.uk/id/eprint/13301

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