Recovering Lost Details: Multi-Scale Frequency Compensation for Long-Term Time Series Forecasting
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...
| Authors | Runmin Zou, Siyi Xie, Yaohui Huang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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