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New MINT method uses tensor decomposition for time series data mining

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MINT, a new method for analyzing time series data by using tensor decomposition on stacked recurrence matrices. This approach creates dot plots from self-similarity matrices, allowing for the mining of co-clustered patterns. The MINT pipeline has been demonstrated to effectively identify cross-sensor patterns in datasets with regular motifs, showing promise in domains such as transportation and energy demand. AI

IMPACT Introduces a novel tensor decomposition technique for enhanced time series analysis and pattern discovery.

RANK_REASON The item describes a new method and its application presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MINT method uses tensor decomposition for time series data mining

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kaamil Kaka, Audrey Der, Evangelos E. Papalexakis, Zachary Zimmerman, Vikram Jayaram ·

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

    arXiv:2608.04157v1 Announce Type: new 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 …

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