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• MR-CDM significantly outperforms state-of-the-art baselines like CSDI and Informer on time series forecasting tasks.result 90%
• The trend decomposition module is essential for MR-CDM's performance, with its removal causing an 89.6% performance degradation.technical 95%
• Conditioning the diffusion process on historical trends is crucial, with unconditional diffusion suffering a 1280% performance drop.technical 95%
• MR-CDM uses a three-level moving average decomposition with window sizes of 5, 25, and 51 to decouple short-term fluctuations from seasonal periodicity.method 90%
• The framework transforms time series components into 2D image representations using adaptive delay embedding and STFT.method 85%
• Experiments were conducted on the ETTh1 (Electricity Transformer Temperature) dataset among others.result 90%
• The model uses an Enhanced Hierarchical Reconstructor with hierarchical reconstruction, cross-scale attention, and adaptive weighting to recover predictions.method 85%
• Data preprocessing includes linear interpolation for missing values, outlier handling using the 3σ rule, and Z-score normalization.method 90%

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