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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 是一个可解释性系统，利用大语言模型为通过张量分解提取的隐藏模式生成自然语言解释，无需依赖可能不准确的标签或元数据。