WMF-Net: A Self-Organizing Network Traffic Prediction Framework with Wavelet-Mamba-Fourier Integration for Intelligent Resource Management

WMF-Net: A Self-Organizing Network Traffic Prediction Framework with Wavelet-Mamba-Fourier Integration for Intelligent Resource Management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article WMF-Net: A Self-Organizing Network Traffic Prediction Framework with Wavelet-Mamba-Fourier Integration for Intelligent Resource Management Wenhao Li, Jiale Song, Pengying Ouyang, Yicai Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7438060/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Network traffic prediction is essential for intelligent resource management in self-organizing systems, but existing methods struggle to simultaneously capture multi-scale temporal patterns, long-range dependencies, and periodic behaviors while maintaining computational efficiency. This paper presents WMF-Net, a novel traffic prediction framework that synergistically integrates wavelet decomposition, selective state space modeling, and frequency domain processing. The framework introduces four key components: Multi-scale Wavelet Decomposition for hierarchical temporal pattern extraction, Wavelet Traffic Convolution with scale-adaptive mechanisms, Traffic-aware Mamba for efficient long-range dependency modeling, and Fourier Pattern Adjustment for periodic pattern enhancement. WMF-Net employs a comprehensive training objective that balances reconstruction accuracy, temporal consistency, and spectral coherence. Extensive experiments on four real-world traffic datasets (METR-LA, PEMS-BAY, PEMS04, PEMS08) demonstrate consistent improvements over state-of-the-art methods, achieving 1.0-1.3% gains in MAE, 0.6-1.1% in RMSE, and 0.2-1.0% in MAPE across different prediction horizons. Ablation studies reveal that Traffic-aware Mamba provides the largest individual contribution (10.2% MAE reduction), while the complete framework achieves up to 27.1% improvement over baseline approaches. The proposed uncertainty-based fusion mechanism further enhances robustness with 3.2-4.1% additional improvements. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Oct, 2025 Reviews received at journal 05 Oct, 2025 Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers invited by journal 17 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Editor invited by journal 03 Sep, 2025 Submission checks completed at journal 27 Aug, 2025 First submitted to journal 27 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7438060","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":520091452,"identity":"412f940b-9d46-45f6-9cc8-2c2e4de620a7","order_by":0,"name":"Wenhao Li","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Li","suffix":""},{"id":520091453,"identity":"49bcd564-d06c-4d9b-ad2a-dbf8826a5a87","order_by":1,"name":"Jiale Song","email":"","orcid":"","institution":"Hunan University of Arts and 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