co-clustering

This paper introduces MINT, a method that stacks recurrence (self-similarity) matrices of multiple time series into a tensor and applies tensor decomposition to mine co-clustered cross-sensor patterns. Experiments on transit, electricity, wind turbine, and traffic data show effective co-clustering of motifs in regular time series.

AnTenA is an interpretable system that leverages large language models to generate natural language explanations for hidden patterns extracted via tensor decomposition, without relying on potentially inaccurate labels or metadata.