{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/long-term-5g-network-traffic-forecasting-via","title":"Long term 5G network traffic forecasting via modeling non-stationarity with deep learning","arxiv_id":null,"date":"2023-06-06","proceeding":"journal 2023 6","authors":["Yuguang Yang","Shupeng Geng","Baochang Zhang","Juan Zhang","Zheng Wang","Yong Zhang & David Doermann"],"abstract":"5G cellular networks have recently fostered a wide range of emerging applications, but their popularity has led to traffic growth that far outpaces network expansion. This mismatch may decrease network quality and cause severe performance problems. To reduce the risk, operators need long term traffic prediction to perform network expansion schemes months ahead. However, long term prediction horizon exposes the non-stationarity of series data, which deteriorates the performance of existing approaches. We deal with this problem by developing a deep learning model, Diviner, that incorporates stationary processes into a well-designed hierarchical structure and models non-stationary time series with multi-scale stable features. We demonstrate substantial performance improvement of Diviner over the current state of the art in 5G network traffic forecasting with detailed months-level forecasting for massive ports with complex flow patterns. Extensive experiments further present its applicability to various predictive scenarios without any modification, showing potential to address broader engineering problems.","url_abs":"https://www.nature.com/articles/s44172-023-00081-4","url_pdf":"https://www.nature.com/articles/s44172-023-00081-4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"long-term-5g-network-traffic-forecasting-via","repo_url":"https://github.com/CapricornGuang/Diviner-Nonstationary-time-series-forecasting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"},{"task_slug":"traffic-prediction","task_name":"Traffic Prediction"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}