TopoCL: Topological Contrastive Learning for Time Series.

Area of Science:

• Machine Learning
• Data Science
• Time Series Analysis

Background:

• Universal time-series representation learning is crucial for applications like classification, anomaly detection, and forecasting.
• Contrastive learning (CL) shows promise but struggles with data augmentation that can distort temporal dependencies and semantic information.
• Existing methods often lose vital information during the augmentation process.

Purpose of the Study:

• To introduce a novel method, topological contrastive learning for time series (TopoCL), to address information loss in time-series representation learning.
• To integrate topological features, invariant under transformations, into the contrastive learning framework.
• To improve the comprehensive understanding of both temporal semantics and topological properties of time-series data.

Main Methods:

• TopoCL incorporates persistent homology to capture invariant topological characteristics of time-series data.
• Topological features are represented using persistence diagrams (PDs) and encoded by a dedicated neural network.
• The approach jointly optimizes contrastive learning in the time modality and time-topology correspondence.

Main Results:

• TopoCL demonstrated state-of-the-art performance on four diverse downstream tasks: classification, anomaly detection, forecasting, and transfer learning.
• The method effectively mitigates information loss caused by data augmentation in traditional contrastive learning.
• Experimental results validate the efficacy of integrating topological and temporal modalities.

Conclusions:

• TopoCL offers a robust framework for time-series representation learning by leveraging both temporal and topological information.
• The proposed method advances the field by preserving semantic information often lost in conventional contrastive learning techniques.
• TopoCL provides a significant improvement for various time-series analysis applications.