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

TL;DR

This paper introduces MoST, a contrastive learning-based method for disentangling mode-specific and mode-invariant features in tensor time series, improving classification and forecasting performance.

Contribution

MoST is a novel tensor time series representation learning method that disentangles mode-specific and mode-invariant features using contrastive learning.

Findings

MoST outperforms state-of-the-art methods in classification accuracy.

MoST improves forecasting accuracy on real-world datasets.

The method effectively captures mode-specific features in tensor data.

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.