{"ID":2845298,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.04722","arxiv_id":"2511.04722","title":"AWEMixer: Adaptive Wavelet-Enhanced Mixer Network for Long-Term Time Series Forecasting","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","short_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...","url_abs":"https://arxiv.org/abs/2511.04722","url_pdf":"https://arxiv.org/pdf/2511.04722v1","authors":"[\"Qianyang Li\",\"Xingjun Zhang\",\"Peng Tao\",\"Shaoxun Wang\",\"Yancheng Pan\",\"Jia Wei\"]","published":"2025-11-06T11:27:12Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":607362,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2845298,"paper_url":"https://arxiv.org/abs/2511.04722","paper_title":"AWEMixer: Adaptive Wavelet-Enhanced Mixer Network for Long-Term Time Series Forecasting","repo_url":"https://github.com/hit636/AWEMixer","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}