Influence-Based Channel Reweighting for Multivariate Time Series Forecasting

Abstract

There has been an emergence of deep models for multivariate time series forecasting. Transformer-based models and recent linear forecasters are currently battling for the leading position. However, most of them focus on model architectures, while leaving time series data itself underexplored. We argue that not all channels within time series data should share equal importance during the training process since the levels of relevance vary over time. Our goal is to illustrate different levels of contributions made by each channel in time series data via self-influence. To this end, we propose a novel channel reweighting strategy for time series forecasting, IBCR, which employs the ideas of self-influence and clustering to reweigh channels within time series data based on different channel importances. IBCR improves both the explainability of time series data and the predictive performance of baseline models. Extensive experimental results show the reasonability of channel self-influence and the effectiveness of the channel reweighting strategy.