Leveraging Multivariate Long-Term History Representation for Time Series Forecasting

• URL: http://arxiv.org/abs/2505.14737v1
• Date: Tue, 20 May 2025 03:46:36 GMT
• Title: Leveraging Multivariate Long-Term History Representation for Time Series Forecasting
• Authors: Huiliang Zhang, Di Wu, Arnaud Zinflou, Stephane Dellacherie, Mouhamadou Makhtar Dione, Benoit Boulet,
• Abstract summary: We propose a framework called Long-term Multivariate Representation (LMHR) for MTS forecasting.<n>LMHR encodes the long-term history into segment-level contextual representations and reduces point-level noise.<n>It consistently improves prediction accuracy by 9.8% on the top 10% of rapidly changing patterns.
• Score: 6.661358934189792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multivariate Time Series (MTS) forecasting has a wide range of applications in both industry and academia. Recent advances in Spatial-Temporal Graph Neural Network (STGNN) have achieved great progress in modelling spatial-temporal correlations. Limited by computational complexity, most STGNNs for MTS forecasting focus primarily on short-term and local spatial-temporal dependencies. Although some recent methods attempt to incorporate univariate history into modeling, they still overlook crucial long-term spatial-temporal similarities and correlations across MTS, which are essential for accurate forecasting. To fill this gap, we propose a framework called the Long-term Multivariate History Representation (LMHR) Enhanced STGNN for MTS forecasting. Specifically, a Long-term History Encoder (LHEncoder) is adopted to effectively encode the long-term history into segment-level contextual representations and reduce point-level noise. A non-parametric Hierarchical Representation Retriever (HRetriever) is designed to include the spatial information in the long-term spatial-temporal dependency modelling and pick out the most valuable representations with no additional training. A Transformer-based Aggregator (TAggregator) selectively fuses the sparsely retrieved contextual representations based on the ranking positional embedding efficiently. Experimental results demonstrate that LMHR outperforms typical STGNNs by 10.72% on the average prediction horizons and state-of-the-art methods by 4.12% on several real-world datasets. Additionally, it consistently improves prediction accuracy by 9.8% on the top 10% of rapidly changing patterns across the datasets.

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