Transformer-based and MLP-based methods have emerged as leading approaches in
time series forecasting (TSF). While Transformer-based methods excel in
capturing long-range dependencies, they suffer from high computational
complexities and tend to overfit. Conversely, MLP-based methods offer
computational efficiency and adeptness in modeling temporal dynamics, but they
struggle with capturing complex temporal patterns effectively. To address these
challenges, we propose a novel MLP-based Adaptive Multi-Scale Decomposition
(AMD) framework for TSF. Our framework decomposes time series into distinct
temporal patterns at multiple scales, leveraging the Multi-Scale Decomposable
Mixing (MDM) block to dissect and aggregate these patterns in a residual
manner. Complemented by the Dual Dependency Interaction (DDI) block and the
Adaptive Multi-predictor Synthesis (AMS) block, our approach effectively models
both temporal and channel dependencies and utilizes autocorrelation to refine
multi-scale data integration. Comprehensive experiments demonstrate that our
AMD framework not only overcomes the limitations of existing methods but also
consistently achieves state-of-the-art performance in both long-term and
short-term forecasting tasks across various datasets, showcasing superior
efficiency. Code is available at https://github.com/TROUBADOUR000/AMD

我们提出了一种新颖的基于 MLP 的自适应多尺度分解 (AMD) 框架用于时间序列预测，该框架在多个尺度上将时间序列分解为不同的时间模式，并利用多尺度分解混合 (MDM) 模块将这些模式进行拆分和聚合，通过双重依赖交互 (DDI) 模块和自适应多预测合成 (AMS) 模块有效地建模时间和通道依赖性，并利用自相关性来提炼多尺度数据集成。全面的实验表明，我们的 AMD 框架不仅克服了现有方法的局限性，而且在各种数据集上的长期和短期预测任务中始终取得了最先进的性能，展示了出色的效率。