AWEMixer: Adaptive Wavelet-Enhanced Mixer Network for Long-Term Time Series Forecasting

• URL: http://arxiv.org/abs/2511.04722v1
• Date: Thu, 06 Nov 2025 11:27:12 GMT
• Title: AWEMixer: Adaptive Wavelet-Enhanced Mixer Network for Long-Term Time Series Forecasting
• Authors: Qianyang Li, Xingjun Zhang, Peng Tao, Shaoxun Wang, Yancheng Pan, Jia Wei,
• Abstract summary: We propose AWEMixer, an Adaptive Wavelet-Enhanced Mixer Network.<n>A Frequency Router designs to utilize the global periodicity pattern achieved by Fast Fourier Transform to adaptively weight localized wavelet subband.<n>A Coherent Gated Fusion Block to achieve selective integration of prominent frequency features with multi-scale temporal representation.
• Score: 12.450099337354017
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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