MRLCD-A: Lag-aware alignment for multivariate time series forecasting in multiple scenarios

Abstract

In multivariate time series forecasting tasks, the varying degrees of lag relationships among multivariate data significantly increase the complexity of accurate predictions. A model must effectively capture long-term dependencies and address intricate lag correlations to achieve reliable long-term forecasting. This paper proposes a novel Multivariate Rolling Lag Correlation Detection-Alignment (MRLCD-A) method to tackle these challenges. The method identifies rolling correlations, calculates lag distances in multivariate sequence inputs, and aligns the lagged variables accordingly. Multivariate Time Series (MTS) forecasting uses a Channel Dependency (CD) approach. Experiments on time series datasets across various scenarios, including electricity, weather, exchange rates, and atmospheric carbon concentrations, demonstrate that the proposed method outperforms state-of-the-art models in forecasting general multivariate time series and predicting long-term time series data in real-world environments.