用自适应小波增强网络，提升物联网长序列预测精度。
AWEMixer: Adaptive Wavelet-Enhanced Mixer Network for Long-Term Time Series Forecasting
- 引入频域路由机制，动态融合小波子带特征
- 在7个公开数据集上优于SOTA模型，长序列误差更低
- 适合处理非平稳、多尺度的传感器时序数据
物联网环境中的长期时间序列预测仍面临挑战，因传感器信号具有非平稳性和多尺度特性。传统方法局限于时域分析，而傅里叶变换获得的全局频率信息易被误认为平稳信号，模糊瞬态事件的时间模式。本文提出AWEMixer，包含两个创新组件：1）频域路由模块，利用快速傅里叶变换获取的全局周期性模式，自适应加权局部小波子带；2）相干门控融合块，通过交叉注意力与门控机制选择性整合显著频率特征与多尺度时序表示，实现精确的时频定位并保持对噪声的鲁棒性。七个公开基准测试表明，该模型在长序列预测中持续优于基于Transformer和MLP的SOTA模型。代码已开源于https://github.com/hit636/AWEMixer。
原文摘要 · Abstract (English)
Forecasting long-term time series in IoT environments remains a significant challenge due to the non-stationary and multi-scale characteristics of sensor signals. Furthermore, error accumulation causes a decrease in forecast quality when predicting further into the future. Traditional methods are restricted to operate in time-domain, while the global frequency information achieved by Fourier transform would be regarded as stationary signals leading to blur the temporal patterns of transient events. We propose AWEMixer, an Adaptive Wavelet-Enhanced Mixer Network including two innovative components: 1) a Frequency Router designs to utilize the global periodicity pattern achieved by Fast Fourier Transform to adaptively weight localized wavelet subband, and 2) a Coherent Gated Fusion Block to achieve selective integration of prominent frequency features with multi-scale temporal representation through cross-attention and gating mechanism, which realizes accurate time-frequency localization while remaining robust to noise. Seven public benchmarks validate that our model is more effective than recent state-of-the-art models. Specifically, our model consistently achieves performance improvement compared with transformer-based and MLP-based state-of-the-art models in long-sequence time series forecasting. Code is available at https://github.com/hit636/AWEMixer
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