Work

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

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2608.04157

Authors: Kaka, Kaamil, Der, Audrey, Papalexakis, Evangelos E., Zimmerman, Zachary, Jayaram, Vikram

Institutions: The University of Texas at Dallas, University of North Texas at Dallas, University of California, Riverside

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Awards: CAREER: Autonomous Tensor Analysis: From Raw Multi-Aspect Data to Actionable Insights, G6671297155

OpenAlex ID: W7196998436

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