MINT: Tensor Decomposition on Stacked Recurrence Matrices for Time Series Data Mining

Abstract:Recurrence plots are a time series data mining primitive applied to a variety of domains (e.g. star light curves, sound waveforms, CCT telemetry). This work proposes tensorized self-similarity matrices as a primitive for univariate time series datasets ($N\times n$) of $N$ time series of length $n$ with a subsequence window of length $m$, and whose tensor-based nature is naturally extensible to multivariate datasets. The proposed method to compute this primitive computes dot plots of size $N \times (n-m+1) \times (n-m+ 1)$ from these datasets, where the subsequent tensor is mined using tensor decomposition methods to mine for co-clustered patterns. We demonstrate our results in mass rapid transit, electricity demand, wind turbine, and car traffic data, finding the MINT pipeline effectively co-clusters cross-sensor patterns in highly regular datasets containing motifs at regular intervals.

Machine Learning (cs.LG)

arXiv:2608.04157 [cs.LG]

(or arXiv:2608.04157v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.04157 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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