Contrastive learning has shown to be effective to learn representations from
time series in a self-supervised way. However, contrasting similar time series
instances or values from adjacent timestamps within a time series leads to
ignore their inherent correlations, which results in deteriorating the quality
of learned representations. To address this issue, we propose SoftCLT, a simple
yet effective soft contrastive learning strategy for time series. This is
achieved by introducing instance-wise and temporal contrastive loss with soft
assignments ranging from zero to one. Specifically, we define soft assignments
for 1) instance-wise contrastive loss by the distance between time series on
the data space, and 2) temporal contrastive loss by the difference of
timestamps. SoftCLT is a plug-and-play method for time series contrastive
learning that improves the quality of learned representations without bells and
whistles. In experiments, we demonstrate that SoftCLT consistently improves the
performance in various downstream tasks including classification,
semi-supervised learning, transfer learning, and anomaly detection, showing
state-of-the-art performance. Code is available at this repository:
https://github.com/seunghan96/softclt.
SoftCLT: Improving Time Series Contrastive Learning
Time series data analysis is a complex and multi-disciplinary field that requires the integration of various techniques and approaches. One popular method to learn representations from time series data is contrastive learning, which aims to capture the underlying patterns and relationships within the data. However, traditional contrastive learning methods often fail to consider the inherent correlations between similar time series instances or values from adjacent timestamps, resulting in suboptimal representations.
In this article, we introduce SoftCLT, a novel and effective soft contrastive learning strategy specifically designed for time series data. SoftCLT addresses the issue of ignoring inherent correlations by incorporating instance-wise and temporal contrastive losses with soft assignments ranging from zero to one. By measuring the distance between time series instances in the data space for instance-wise contrastive loss and considering the difference of timestamps for temporal contrastive loss, SoftCLT captures both local similarities and temporal dependencies in the data.
One key advantage of SoftCLT is its simplicity and ease of implementation. It can be seamlessly integrated into existing contrastive learning frameworks as a plug-and-play method without requiring additional complexities. This makes it a practical solution for improving the quality of learned representations without the need for extensive modifications.
Our experiments demonstrate the effectiveness of SoftCLT in various downstream tasks such as classification, semi-supervised learning, transfer learning, and anomaly detection. In each task, SoftCLT consistently outperforms existing methods, showcasing state-of-the-art performance in time series analysis. The availability of the code in the provided repository ensures reproducibility and facilitates further research and development in this area.
In conclusion, SoftCLT represents an important advancement in time series contrastive learning. Its ability to capture inherent correlations and improve representation quality without introducing unnecessary complexities makes it a valuable tool for researchers and practitioners in the field. By considering the multi-disciplinary nature of time series analysis and incorporating insights from different domains, SoftCLT sets the stage for further advancements and opens up new possibilities for understanding and utilizing time series data.
Code repository: https://github.com/seunghan96/softclt