Adaptive Resolution Loss: An Efficient and Effective Loss for Time Series Hierarchical Contrastive Self-Supervised Learning Framework

Time series data is a crucial form of information that has vast opportunities. With the widespread use of sensor networks, large-scale time series data has become ubiquitous. One of the current state-of-the-art SSL frameworks in time series is called TS2Vec. TS2Vec specially designs a hierarchical contrastive learning framework that uses loss-based training, which performs outstandingly against benchmark testing. However, the computational cost for TS2Vec is often significantly greater than other SSL frameworks. In this paper, we present a new self-supervised learning loss named, adaptive resolution loss. The proposed solution reduces the number of resolutions used for training the model via an adaptive selection score, leading to an efficient adaptive resolution loss based learning algorithm. In the experiment, we demonstrate that the proposed method preserves the original model's integrity while significantly enhancing its training time.