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

arXiv: https://arxiv.org/abs/2608.04157 · HTML: https://mlsanity.com/paper/2608.04157/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [Tensor Decomposition for Signal Processing and Machine Learning](https://mlsanity.com/paper/1607.01668/) (2016)
- [LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting](https://mlsanity.com/paper/2306.08259/) (2023)
- [TensorLy: Tensor Learning in Python](https://mlsanity.com/paper/1610.09555/) (2016)
- [Modelling matrix time series via a tensor CP-decomposition](https://mlsanity.com/paper/2112.15423/) (2021)
- [Joint Background-Anomaly-Noise Decomposition for Robust Hyperspectral Anomaly Detection via Constrained Convex Optimization](https://mlsanity.com/paper/2401.14814/) (2024)

## Abstract (arXiv)

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× 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 × (n-m+1) × (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.