Dynamic Decomposition Diffusion Model for Probabilistic Multivariate Time Series Forecasting

Yuxin Li

doi:10.1109/tsp.2026.3733192

Dynamic Decomposition Diffusion Model for Probabilistic Multivariate Time Series Forecasting

2026-09-11

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DOI：10.1109/tsp.2026.3733192

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Accurate probabilistic forecasting of multivariate time series (MTS) is crucial for many real-world applications but remains challenging due to the inherent complexity and non-stationarity of these signals. To address these challenges, we propose the Dynamic Decomposition Diffusion Model (D3M), which integrates advanced signal decomposition techniques with diffusion-based generative models. D3M dynamically decomposes MTS into narrowband intrinsic mode functions (IMFs) and employs a patching channel-independence transformer to efficiently manage the increased data dimensionality. A conditional diffusion model estimates the probabilistic distributions of the decomposed components, with the final MTS distribution obtained as the joint distribution of all components. These modules are seamlessly unified within a Bayesian joint optimization framework, improving forecasting accuracy and uncertainty quantification. Experimental results on six real-world datasets confirm that D3M consistently outperforms state-of-the-art methods in both accuracy and robustness.

Keywords:

Bayes methods

diffusion models

empirical mode decomposition

forecast uncertainty

time series analysis

Dynamic Decomposition Diffusion Model for Probabilistic Multivariate Time Series Forecasting

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