用小模型实现高精度时间序列预测，兼顾周期与非平稳特征。
Wavelet Mixture of Experts for Time Series Forecasting
- 结合小波变换与MLP，在小波域捕捉周期与非平稳特性。
- 引入专家混合机制，高效处理多通道依赖关系，参数量少但性能强。
- 在8个真实数据集上达到当前最优，特别适合多通道时间序列预测。
时间序列预测领域快速发展，近期大规模Transformer和轻量级MLP模型表现出色。然而，传统Transformer参数量大且难以通过平滑捕捉数据中的非平稳特征；而MLP模型则难以有效处理多通道依赖。为此，我们提出一种新型轻量级预测模型WaveTS-B，将小波变换与MLP结合，以在小波域中同时捕捉数据的周期性和非平稳性特征。在此基础上，我们设计了一种通道聚类策略，引入专家混合（MoE）框架，利用门控机制与专家网络高效处理多通道依赖。进一步提出针对多通道时间序列的WaveTS-M模型。在八个真实世界时间序列数据集上的实证评估表明，WaveTS系列模型在显著减少参数量的同时达到当前最优性能。尤其在多通道数据集上，WaveTS-M表现大幅提升，验证了其有效性。
原文摘要 · Abstract (English)
The field of time series forecasting is rapidly advancing, with recent large-scale Transformers and lightweight Multilayer Perceptron (MLP) models showing strong predictive performance. However, conventional Transformer models are often hindered by their large number of parameters and their limited ability to capture non-stationary features in data through smoothing. Similarly, MLP models struggle to manage multi-channel dependencies effectively. To address these limitations, we propose a novel, lightweight time series prediction model, WaveTS-B. This model combines wavelet transforms with MLP to capture both periodic and non-stationary characteristics of data in the wavelet domain. Building on this foundation, we propose a channel clustering strategy that incorporates a Mixture of Experts (MoE) framework, utilizing a gating mechanism and expert network to handle multi-channel dependencies efficiently. We propose WaveTS-M, an advanced model tailored for multi-channel time series prediction. Empirical evaluation across eight real-world time series datasets demonstrates that our WaveTS series models achieve state-of-the-art (SOTA) performance with significantly fewer parameters. Notably, WaveTS-M shows substantial improvements on multi-channel datasets, highlighting its effectiveness.
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