Impact of Negative Sample Scaling on Time-Series Forecasting Accuracy in Self-Supervised Contrastive Learning

In recent years, the introduction of self-supervised contrastive learning (SSCL) has demonstrated remarkable improvements in representation learning across various domains, including natural language processing and computer vision. By leveraging the inherent benefits of self-supervision, SSCL enables the pre-training of representation models using vast amounts of unlabeled data. Despite these advances, there remains a significant gap in understanding the impact of different SSCL strategies on time series forecasting performance, as well as the specific benefits that SSCL can bring. This paper

Research goal: What is the impact of scaling the number of negative samples in self-supervised contrastive learning on the downstream forecasting accuracy of time-series models evaluated on the Monash University Time Series Repository?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.7/10.