Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series

• URL: http://arxiv.org/abs/2410.12606v2
• Date: Mon, 21 Oct 2024 06:27:47 GMT
• Title: Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series
• Authors: Ching Chang, Chiao-Tung Chan, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen,
• Abstract summary: TimeDRL is a framework for multivariate time-series representation learning with dual-level disentangled embeddings. TimeDRL features: (i) timestamp-level and instance-level embeddings using a [] token strategy; (ii) timestamp-predictive and instance-contrastive tasks for representation learning; and (iii) avoidance of augmentation methods to eliminate inductive biases. Experiments on forecasting and classification datasets show TimeDRL outperforms existing methods, with further validation in semi-supervised settings with limited labeled data.
• Score: 10.99576829280084
• License:
• Abstract: Multivariate time-series data in fields like healthcare and industry are informative but challenging due to high dimensionality and lack of labels. Recent self-supervised learning methods excel in learning rich representations without labels but struggle with disentangled embeddings and inductive bias issues like transformation-invariance. To address these challenges, we introduce TimeDRL, a framework for multivariate time-series representation learning with dual-level disentangled embeddings. TimeDRL features: (i) disentangled timestamp-level and instance-level embeddings using a [CLS] token strategy; (ii) timestamp-predictive and instance-contrastive tasks for representation learning; and (iii) avoidance of augmentation methods to eliminate inductive biases. Experiments on forecasting and classification datasets show TimeDRL outperforms existing methods, with further validation in semi-supervised settings with limited labeled data.

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