Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning

• URL: http://arxiv.org/abs/2602.23663v1
• Date: Fri, 27 Feb 2026 04:08:51 GMT
• Title: Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning
• Authors: Kohei Obata, Taichi Murayama, Zheng Chen, Yasuko Matsubara, Yasushi Sakurai,
• Abstract summary: Multi-mode tensor time series (TTS) can be found in many domains, such as search engines and environmental monitoring systems.<n>We propose a novel representation learning method designed specifically for TTS, namely MoST.<n>MoST uses a tensor slicing approach to reduce the complexity of the TTS structure and learns representations that can be disentangled into individual non-temporal modes.
• Score: 17.909123818819292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-mode tensor time series (TTS) can be found in many domains, such as search engines and environmental monitoring systems. Learning representations of a TTS benefits various applications, but it is also challenging since the complexities inherent in the tensor hinder the realization of rich representations. In this paper, we propose a novel representation learning method designed specifically for TTS, namely MoST. Specifically, MoST uses a tensor slicing approach to reduce the complexity of the TTS structure and learns representations that can be disentangled into individual non-temporal modes. Each representation captures mode-specific features, which are the relationship between variables within the same mode, and mode-invariant features, which are in common in representations of different modes. We employ a contrastive learning framework to learn parameters; the loss function comprises two parts intended to learn representation in a mode-specific way and mode-invariant way, effectively exploiting disentangled representations as augmentations. Extensive experiments on real-world datasets show that MoST consistently outperforms the state-of-the-art methods in terms of classification and forecasting accuracy. Code is available at https://github.com/KoheiObata/MoST.

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