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This approach employs SG filters to preprocess and attenuate high-frequency noise, and utilizes SWT for the multi-resolution decomposition of low-frequency trends and high-frequency fluctuations. Additionally, the model incorporates a dual-path neural network architecture, comprising a one-dimensional Convolutional Neural Network (CNN) and an attention-enhanced LSTM. This architecture is designed to extract local patterns and model long-term dependencies. Moreover, the introduction of a frequency-aware hierarchical contrastive learning framework significantly enhances the model\u2019s generalization capabilities for non-stationary data. Experimental evaluations conducted on public cloud task datasets confirm that the SWT-CLSTM model outperforms traditional methods and prevailing deep learning models across various time granularities, thereby markedly enhancing the temporal prediction accuracy of cloud computing resource scheduling.<\/jats:p>","DOI":"10.1007\/s44443-025-00316-8","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T10:40:58Z","timestamp":1763462458000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SWT-CLSTM: A hybrid model for cloud workload prediction combining smooth wavelet transform and contrastive learning"],"prefix":"10.1007","volume":"37","author":[{"given":"Biying","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7687-7254","authenticated-orcid":false,"given":"Guanghao","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Qinghe","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"316_CR1","doi-asserted-by":"publisher","unstructured":"A N, Sunitha KA (2022) Optimization of wavelet decomposition level for synthetic ECG signal denoising analysis. 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