A Decomposition-based State Space Model for Multivariate Time-Series Forecasting
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis
Time series data, including univariate and multivariate ones, are characterized by unique composition and complex multi-scale temporal variations. They often require special consideration of decomposition and multi-scale…
Anomaly DetectionImputationTime SeriesTime Series AnalysisScale-Aware Neural Architecture Search for Multivariate Time Series Forecasting
Multivariate time series (MTS) forecasting has attracted much attention in many intelligent applications. It is not a trivial task, as we need to consider both intra-variable dependencies and inter-variable dependencies.…
Graph LearningMultivariate Time Series ForecastingNeural Architecture SearchTime Series+2Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
Predicting multivariate time series is crucial, demanding precise modeling of intricate patterns, including inter-series dependencies and intra-series variations. Distinctive trend characteristics in each time series pos…
Multivariate Time Series ForecastingTime SeriesTime Series ForecastingBayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
In real-world scenarios like traffic and energy, massive time-series data with missing values and noises are widely observed, even sampled irregularly. While many imputation methods have been proposed, most of them work …
Computational EfficiencyGaussian ProcessesImputationMissing Values+3TableTime: Reformulating Time Series Classification as Zero-Shot Table Understanding via Large Language Models
Large language models (LLMs) have demonstrated their effectiveness in multivariate time series classification (MTSC). Effective adaptation of LLMs for MTSC necessitates informative data representations. Existing LLM-base…
Problem DecompositionTime SeriesTime Series Classificationzero-shot-classification+1