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Self-Supervised Learning for Time Series: Contrastive or Generative?

Ziyu Liu, Azadeh Alavi, Minyi Li, Xiang Zhang

TL;DR

This paper introduces the basic frameworks for contrastive and generative SSL, respectively, and discusses how to obtain the supervision signal that guides the model optimization, and conducts a comparative analysis in fair settings.

Abstract

Self-supervised learning (SSL) has recently emerged as a powerful approach to learning representations from large-scale unlabeled data, showing promising results in time series analysis. The self-supervised representation learning can be categorized into two mainstream: contrastive and generative. In this paper, we will present a comprehensive comparative study between contrastive and generative methods in time series. We first introduce the basic frameworks for contrastive and generative SSL, respectively, and discuss how to obtain the supervision signal that guides the model optimization. We then implement classical algorithms (SimCLR vs. MAE) for each type and conduct a comparative analysis in fair settings. Our results provide insights into the strengths and weaknesses of each approach and offer practical recommendations for choosing suitable SSL methods. We also discuss the implications of our findings for the broader field of representation learning and propose future research directions. All the code and data are released at \url{https://github.com/DL4mHealth/SSL_Comparison}.

Self-Supervised Learning for Time Series: Contrastive or Generative?

TL;DR

This paper introduces the basic frameworks for contrastive and generative SSL, respectively, and discusses how to obtain the supervision signal that guides the model optimization, and conducts a comparative analysis in fair settings.

Abstract

Self-supervised learning (SSL) has recently emerged as a powerful approach to learning representations from large-scale unlabeled data, showing promising results in time series analysis. The self-supervised representation learning can be categorized into two mainstream: contrastive and generative. In this paper, we will present a comprehensive comparative study between contrastive and generative methods in time series. We first introduce the basic frameworks for contrastive and generative SSL, respectively, and discuss how to obtain the supervision signal that guides the model optimization. We then implement classical algorithms (SimCLR vs. MAE) for each type and conduct a comparative analysis in fair settings. Our results provide insights into the strengths and weaknesses of each approach and offer practical recommendations for choosing suitable SSL methods. We also discuss the implications of our findings for the broader field of representation learning and propose future research directions. All the code and data are released at \url{https://github.com/DL4mHealth/SSL_Comparison}.
Paper Structure (29 sections, 2 equations, 3 figures, 1 table)

This paper contains 29 sections, 2 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Self-Supervised Learning for Time Series
  4. Contrastive SSL
  5. Generative SSL
  6. Preliminaries and Problem Formulation
  7. Contrastive Representation Learning
  8. Overview
  9. Pre-training
  10. Contrastive loss function
  11. Fine-tuning
  12. Generative Representation Learning
  13. Overview
  14. Pre-training
  15. Genrative loss functions
  16. ...and 14 more sections

Figures (3)

  • Figure 1: Frameworks of contrastive and generative self-supervised representation learning. The downstream classification task in fine-tuning can be easily extended to other tasks such as forecasting and clustering. In MAE, the gray blocks refer to the masked-out patches.
  • Figure 2: Performance on the test set (label ratio = 0.1; 100 epochs). (a) Comparison of SimCLR's performance with and without pre-training. (b) Comparison of MAE's performance with and without pre-training. (c) A comparative view of SimCLR and MAE, both with pre-training.
  • Figure 3: Scalability of SSL Models: Both SSL models demonstrate a significant performance increase when the label ratio escalates from 0.01 to 0.1, and subsequently maintain stability for label ratios exceeding 0.3.