iBACon: imBalance-Aware Contrastive Learning for Time Series Forecasting

Abstract

Time series forecasting (TSF) has gained significant attention as a widely explored research area in diverse applications. Existing methods, which focus on improvements in the most common scenarios, focus little on performance in rare cases. Despite their scarce occurrences in the data, these rare samples are more challenging and easily overlooked by models, significantly contributing to the total loss. In this paper, we propose a novel approach (dubbed iBACon) that overcomes this limitation by employing imbalance-aware contrastive learning and trend-seasonal decomposition architecture, specifically designed to solve TSF. To this end, we first introduce the Input-Output Difference (IOD) metric as a pseudo-label and reveal the data imbalance phenomenon in TSF. This label continuity inherently provides a meaningful distance between targets, implying a similarity between nearby targets in both label and feature spaces. Based on this similarity, the proposed imbalance-aware contrastive loss aims to reshape feature embeddings to facilitate knowledge dissemination among challenging samples and learn specific predictive features. Finally, when combined with our trend-seasonal decomposition network, iBACon significantly improves TSF accuracy. Experiments show that iBACon enhances overall average accuracy and substantially improves the 1-3% most challenging samples.