Multi-dimensional time-series subsequence clustering for visual feature analysis of blazar observation datasets

Exploring hidden structures in subsequences extracted from long-term time-series data is one of the primary tasks of time-series data analysis. Clustering is one of the most commonly used techniques in that context; however, various existing issues must be addressed, such as the way to extract less-overlapping subsequences and the definition of the inter-subsequence similarity. Especially in multi-dimensional data analysis, correlations among variables should also be emphasized. To boost users’ exploration of the universalities of subsequences, we incorporate multi-dimensional time-series subsequence clustering methods and visual clustering analysis interface into TimeTubesX, which is an integrated visual analytics environment for multi-dimensional time-dependent observation datasets of blazars. TimeTubesX extracts and filters subsequences with various lengths according to the characteristics of the data and clusters them automatically in consideration of correlations among observed attributes. And then, it allows users to visually examine the clustering results in terms of the cluster features, intercluster transitions, and temporal distributions of clusters. Through the application to two practical case studies, we demonstrate how the enhanced TimeTubesX enables users to see not only instances but also universalities (i.e., time-series motifs or cluster prototypes) in time-series observations of blazars.