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Abstract
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Time series forecasting presents significant challenges in predicting complex temporal patterns across varying horizons. We introduce TimeFlowDiffuser, a novel framework that adapts diffusion models for time series forecasting through a hierarchical structure with adaptive context sampling. Our approach incorporates: (1) a Hierarchical Temporal Resolution module that processes time series at multiple scales; (2) an Adaptive Context Sampling mechanism that dynamically selects relevant historical context; (3) a Frequency-Aware Conditioning component that handles different frequency components; and (4) a Multi-Horizon Generation strategy for efficient prediction at various time horizons. Experiments on five benchmark datasets demonstrate that TimeFlowDiffuser consistently outperforms state-of-the-art methods, achieving average improvements of 7.2% in MSE and 6.3% in MAE, with particularly strong performance on long-horizon forecasting tasks. Our approach shows enhanced robustness to missing values and distributional shifts, with computational trade-offs discussed in Sect. 5.6.
Keywords:
Time series forecasting
Diffusion models
Multi-scale modeling
Adaptive sampling
Multi-horizon prediction
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