多分辨率时间序列生成框架，支持变长输入并提升预测精度
MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations
- 分层多尺度趋势分解+自适应嵌入，处理变长时间序列
- 在4个真实数据集上MAE和RMSE降低6-10%
- 适合需要高精度多尺度时序建模的工业场景
时间序列预测在多个领域至关重要，但现有模型存在输入长度固定和多尺度建模不足的问题。我们提出MR-CDM框架，结合分层多分辨率趋势分解、可变长度输入的自适应嵌入机制以及多尺度条件扩散过程。在四个真实世界数据集上的评估表明，MR-CDM显著优于当前最优基线（如CSDI、Informer），MAE和RMSE降低约6-10%。
原文摘要 · Abstract (English)
Time series forecasting is vital across many domains, yet existing models struggle with fixed-length inputs and inadequate multi-scale modeling. We propose MR-CDM, a framework combining hierarchical multi-resolution trend decomposition, an adaptive embedding mechanism for variable-length inputs, and a multi-scale conditional diffusion process. Evaluations on four real-world datasets demonstrate that MR-CDM significantly outperforms state-of-the-art baselines (e.g., CSDI, Informer), reducing MAE and RMSE by approximately 6-10 to a certain degree.
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