A Decomposition-based State Space Model for Multivariate Time-Series Forecasting

• URL: http://arxiv.org/abs/2602.05389v1
• Date: Thu, 05 Feb 2026 07:17:08 GMT
• Title: A Decomposition-based State Space Model for Multivariate Time-Series Forecasting
• Authors: Shunya Nagashima, Shuntaro Suzuki, Shuitsu Koyama, Shinnosuke Hirano,
• Abstract summary: We propose an end-to-end decomposition framework using three parallel deep state space model branches to capture trend, seasonal, and residual components.<n>Across standard benchmarks, DecompSSM outperformed strong baselines, indicating the effectiveness of combining component-wise deep state space models and global context refinement.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multivariate time series (MTS) forecasting is crucial for decision-making in domains such as weather, energy, and finance. It remains challenging because real-world sequences intertwine slow trends, multi-rate seasonalities, and irregular residuals. Existing methods often rely on rigid, hand-crafted decompositions or generic end-to-end architectures that entangle components and underuse structure shared across variables. To address these limitations, we propose DecompSSM, an end-to-end decomposition framework using three parallel deep state space model branches to capture trend, seasonal, and residual components. The model features adaptive temporal scales via an input-dependent predictor, a refinement module for shared cross-variable context, and an auxiliary loss that enforces reconstruction and orthogonality. Across standard benchmarks (ECL, Weather, ETTm2, and PEMS04), DecompSSM outperformed strong baselines, indicating the effectiveness of combining component-wise deep state space models and global context refinement.

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