发表机构
Central South University（中南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对长期时间序列预测中多尺度方法因下采样丢失细节的问题，提出MWMixer模型，通过双向频带混合和动态融合恢复细节，并在七个数据集上取得竞争性能。
AI 中文摘要
长期时间序列预测通过利用多尺度信息来捕获分层时间模式并建模长距离依赖关系，已经取得了显著进展。然而，现有方法中的时间下采样不可避免地平滑了详细的时间波动，而这种信息丢失由于它们对跨尺度主导趋势的强调而进一步加剧，导致表示表达能力不足。为了解决这个问题，我们提出了一种多尺度小波混合（MWMixer）模型，该模型结合了双向频带混合策略来恢复跨尺度的丢失时间细节，从而实现互补的跨尺度信息交互。然后，一个动态尺度自适应融合模块学习每个尺度的时变权重，将多尺度预测融合为最终预测，增强了多尺度聚合的灵活性。此外，一个跨尺度一致性损失将每个粗尺度预测与区间平均的细尺度输出对齐，而一个多尺度监督损失强制每个尺度的预测准确性，促进跨尺度的一致性学习。在七个真实世界数据集上的大量实验表明，MWMixer在长期预测中实现了有竞争力的性能。
英文摘要
Long-term time series forecasting has made significant progress by leveraging multi-scale information to capture hierarchical temporal patterns and model long-range dependencies. However, temporal downsampling in existing multi-scale methods inevitably smooths detailed temporal fluctuations, and this information loss is further aggravated by their emphasis on dominant trends across scales, resulting in insufficiently expressive representations. To address this, we propose a Multi-Scale Wavelet Mixing (MWMixer) model, which incorporates a Bidirectional Frequency-Bands Mixing strategy to recover lost temporal details across scales, enabling complementary cross-scale information interactions. Then, a Dynamic Scale-Adaptive Fusion module learns time-varying weights for each scale to fuse multi-scale forecasts into the final prediction, enhancing the flexibility of multi-scale aggregation. In addition, a cross-scale consistency loss aligns each coarse-scale prediction with the interval-averaged fine-scale outputs, while a multi-scale supervision loss enforces prediction accuracy at each scale, promoting consistent learning across scales. Extensive experiments on seven real-world datasets demonstrate that MWMixer achieves competitive performance in long-term forecasting.
Comments11 pages. Accepted at the 35th ACM International Conference on Information and Knowledge Management (CIKM 2026)
