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

Abstract

Abstract: 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 Question

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?

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