Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder

Understand

In this work, we investigate the time series representation learning problem using self-supervised techniques.

• Contrastive learning is well-known in this area as it is a powerful method for extracting information from the series and generating task-appropriate representations.
• Despite its proficiency in capturing time series characteristics, these techniques often overlook a critical factor - the inherent noise in this type of data, a consideration usually emphasized in general time series analysis.
• Moreover, there is a notable absence of attention to developing efficient yet lightweight encoder architectures, with an undue focus on delivering contrastive losses.