PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series

• URL: http://arxiv.org/abs/2509.05478v1
• Date: Fri, 05 Sep 2025 20:10:09 GMT
• Title: PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series
• Authors: Jia Wang, Xiao Wang, Chi Zhang,
• Abstract summary: We propose PLanTS, a periodicity-aware self-supervised learning framework that explicitly models irregular latent states and their transitions.<n> PLanTS consistently improves the representation quality over existing SSL methods and demonstrates superior runtime efficiency compared to DTW-based methods.
• Score: 10.332959619473652
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multivariate time series (MTS) are ubiquitous in domains such as healthcare, climate science, and industrial monitoring, but their high dimensionality, limited labeled data, and non-stationary nature pose significant challenges for conventional machine learning methods. While recent self-supervised learning (SSL) approaches mitigate label scarcity by data augmentations or time point-based contrastive strategy, they neglect the intrinsic periodic structure of MTS and fail to capture the dynamic evolution of latent states. We propose PLanTS, a periodicity-aware self-supervised learning framework that explicitly models irregular latent states and their transitions. We first designed a period-aware multi-granularity patching mechanism and a generalized contrastive loss to preserve both instance-level and state-level similarities across multiple temporal resolutions. To further capture temporal dynamics, we design a next-transition prediction pretext task that encourages representations to encode predictive information about future state evolution. We evaluate PLanTS across a wide range of downstream tasks-including multi-class and multi-label classification, forecasting, trajectory tracking and anomaly detection. PLanTS consistently improves the representation quality over existing SSL methods and demonstrates superior runtime efficiency compared to DTW-based methods.

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