Learning by Shifting: Temporal View Construction for Time Series Contrastive Learning

2026-06-20

AI Summary

This work addresses the limitations of handcrafted augmentation strategies in time series contrastive learning, which often introduce spurious correlations and suffer from poor generalization. To overcome these issues, the authors propose a novel paradigm that explicitly encodes temporal shift invariance to construct deterministic views, thereby replacing conventional domain-knowledge-dependent augmentations. This approach leverages temporal shift invariance alone to generate effective positive and negative sample pairs, significantly reducing reliance on manual intervention. Evaluated across six real-world benchmarks and the UCR/UEA archive, the method achieves state-of-the-art performance while substantially accelerating training. Furthermore, the study systematically investigates the impact of batch size and the number of negative samples on model effectiveness, offering valuable insights into the design of contrastive learning frameworks for time series data.

Abstract

Supervised learning demands large quantities of labeled data, a bottleneck that is expensive and reliant on domain-specific expertise. Self-supervised learning, particularly contrastive learning, has emerged as a compelling alternative, enabling rich representation learning directly from unlabeled data. Yet its success hinges critically on the design of positive and negative sample pairs. Existing approaches for time series rely on hand-crafted augmentations and masking heuristics that embed strong domain assumptions, often limiting generalization across diverse temporal patterns and potentially introducing spurious correlations. In this work, we challenge this paradigm by demonstrating that explicitly encoding temporal shift invariance through a simple, deterministic view construction is sufficient to learn strong representations for time series classification. By exploiting temporal structure, our method, Shift Invariant Feature Training (ShiFT), achieves state-of-the-art performance on six diverse real-world time series benchmark datasets, as well as the UCR and UEA archives, while reducing training time. Beyond empirical performance, we present a systematic analysis of contrastive learning dynamics in time series settings, examining the effects of batch size and the number of negatives on downstream performance. Our findings provide practical insights for designing efficient contrastive learning frameworks for time series representation learning. The source code is publicly available at https://github.com/sfi-norwai/ShiFT.

Problem

• time series
• contrastive learning
• self-supervised learning
• data augmentation
• temporal shift invariance

Innovation

• temporal shift invariance
• contrastive learning
• time series representation learning
• self-supervised learning
• view construction