LagTS: Toward Adaptive Lag Relationship Modeling for Multivariate Time Series Forecasting

Abstract

Multivariate time series forecasting has become increasingly crucial in fields such as energy and transportation. Recent research has focused on local lag relationships across variates, yielding impressive results. However, these methods typically require pre-calculating lag indicators and steps between variates based on historical data. This reliance on pre-calculated lag steps neglects the potential variability in lag steps over the historical and predicted time series. In this paper, we propose LagTS, a novel method that adaptively models lag relationships across variates in multivariate time series data and effectively leverages these relationships to improve prediction accuracy. Specifically, the proposed method extracts the lag relationships using a dedicated module. Moreover, it employs a lag relation loss to facilitate the adaptive modeling of the lag relationships. Extensive experimental results on four publicly available datasets and an industry dataset demonstrate the effectiveness of the proposed method.