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MRLCD-A: Lag-aware alignment for multivariate time series forecasting in multiple scenarios
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DOI：10.1016/j.ipm.2025.104191.png)
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Abstract
En 中文
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 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.
Keywords:
Deep learning
Time series
Forecasting
Lag
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