{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:36:57Z","timestamp":1772044617380,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819687244","type":"print"},{"value":"9789819687251","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-8725-1_30","type":"book-chapter","created":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T14:23:21Z","timestamp":1750602201000},"page":"370-381","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Generative Pretrained Transformer for\u00a0Wireless Traffic Prediction"],"prefix":"10.1007","author":[{"given":"Dongjiao","family":"Sun","sequence":"first","affiliation":[]},{"given":"Chuanting","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jingping","family":"Qiao","sequence":"additional","affiliation":[]},{"given":"Tiantian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Haixia","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,21]]},"reference":[{"issue":"9","key":"30_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-021-04761-8","volume":"3","author":"T Deng","year":"2021","unstructured":"Deng, T., Wan, M., Shi, K., Zhu, L., Wang, X., Jiang, X.: Short term prediction of wireless traffic based on tensor decomposition and recurrent neural network. SN Appl. Sci. 3(9), 1\u201314 (2021). https:\/\/doi.org\/10.1007\/s42452-021-04761-8","journal-title":"SN Appl. Sci."},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Gao, Y., Zhang, M., Chen, J., Han, J., Li, D., Qiu, R.: Accurate load prediction algorithms assisted with machine learning for network traffic. In: 2021 International Wireless Communications and Mobile Computing (IWCMC), pp. 1683\u20131688. IEEE (2021)","DOI":"10.1109\/IWCMC51323.2021.9498910"},{"issue":"6","key":"30_CR3","doi-asserted-by":"publisher","first-page":"3899","DOI":"10.1109\/TWC.2017.2689772","volume":"16","author":"R Li","year":"2017","unstructured":"Li, R., Zhao, Z., Zheng, J., Mei, C., Cai, Y., Zhang, H.: The learning and prediction of application-level traffic data in cellular networks. IEEE Trans. Wirel. Commun. 16(6), 3899\u20133912 (2017). https:\/\/doi.org\/10.1109\/TWC.2017.2689772","journal-title":"IEEE Trans. Wirel. Commun."},{"issue":"5","key":"30_CR4","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1109\/MWC.2017.1600304WC","volume":"24","author":"R Li","year":"2017","unstructured":"Li, R., et al.: Intelligent 5G: when cellular networks meet artificial intelligence. IEEE Wirel. Commun. 24(5), 175\u2013183 (2017). https:\/\/doi.org\/10.1109\/MWC.2017.1600304WC","journal-title":"IEEE Wirel. Commun."},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Liu, C., Hoi, S.C., Zhao, P., Sun, J.: Online ARIMA algorithms for time series prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a030 (2016)","DOI":"10.1609\/aaai.v30i1.10257"},{"issue":"2","key":"30_CR6","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/MCI.2009.932254","volume":"4","author":"NI Sapankevych","year":"2009","unstructured":"Sapankevych, N.I., Sankar, R.: Time series prediction using support vector machines: a survey. IEEE Comput. Intell. Mag. 4(2), 24\u201338 (2009). https:\/\/doi.org\/10.1109\/MCI.2009.932254","journal-title":"IEEE Comput. Intell. Mag."},{"key":"30_CR7","doi-asserted-by":"publisher","unstructured":"Shankar, S., Deepika, G., Devi, G., Ramesh, S., Srivastava, S., Kumar, S.S.: Development of efficient wireless sensor network for IoT applications. In: 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN), pp. 1419\u20131424 (2023). https:\/\/doi.org\/10.1109\/ICPCSN58827.2023.00237","DOI":"10.1109\/ICPCSN58827.2023.00237"},{"issue":"8","key":"30_CR8","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1109\/LWC.2021.3078745","volume":"10","author":"W Shen","year":"2021","unstructured":"Shen, W., Zhang, H., Guo, S., Zhang, C.: Time-wise attention aided convolutional neural network for data-driven cellular traffic prediction. IEEE Wirel. Commun. Lett. 10(8), 1747\u20131751 (2021). https:\/\/doi.org\/10.1109\/LWC.2021.3078745","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"30_CR9","first-page":"22419","volume":"34","author":"H Wu","year":"2021","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Adv. Neural. Inf. Process. Syst. 34, 22419\u201322430 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"30_CR10","doi-asserted-by":"publisher","unstructured":"Wu, J., Qiu, T., Tang, H., Liu, X.: Network traffic prediction based on a CNN-LSTM with attention mechanism. In: 2022 7th International Conference on Computational Intelligence and Applications (ICCIA), pp. 205\u2013209 (2022). https:\/\/doi.org\/10.1109\/ICCIA55271.2022.9828411","DOI":"10.1109\/ICCIA55271.2022.9828411"},{"key":"30_CR11","doi-asserted-by":"publisher","unstructured":"Xue, N., Triguero, I., Figueredo, G.P., Landa-Silva, D.: Evolving deep CNN-LSTMs for inventory time series prediction. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 1517\u20131524 (2019). https:\/\/doi.org\/10.1109\/CEC.2019.8789957","DOI":"10.1109\/CEC.2019.8789957"},{"key":"30_CR12","doi-asserted-by":"publisher","unstructured":"Zhang, C., Dang, S., Shihada, B., Alouini, M.S.: Dual attention-based federated learning for wireless traffic prediction. In: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, pp. 1\u201310 (2021). https:\/\/doi.org\/10.1109\/INFOCOM42981.2021.9488883","DOI":"10.1109\/INFOCOM42981.2021.9488883"},{"key":"30_CR13","doi-asserted-by":"publisher","unstructured":"Zhang, C., Zhang, H., Dang, S., Shihada, B., Alouini, M.S.: Gradient compression and correlation driven federated learning for wireless traffic prediction. IEEE Trans. Cogn. Commun. Netw. 1\u20131 (2024). https:\/\/doi.org\/10.1109\/TCCN.2024.3524183","DOI":"10.1109\/TCCN.2024.3524183"},{"issue":"6","key":"30_CR14","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1109\/JSAC.2019.2904363","volume":"37","author":"C Zhang","year":"2019","unstructured":"Zhang, C., Zhang, H., Qiao, J., Yuan, D., Zhang, M.: Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data. IEEE J. Sel. Areas Commun. 37(6), 1389\u20131401 (2019). https:\/\/doi.org\/10.1109\/JSAC.2019.2904363","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"8","key":"30_CR15","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1109\/LCOMM.2018.2841832","volume":"22","author":"C Zhang","year":"2018","unstructured":"Zhang, C., Zhang, H., Yuan, D., Zhang, M.: Citywide cellular traffic prediction based on densely connected convolutional neural networks. IEEE Commun. Lett. 22(8), 1656\u20131659 (2018). https:\/\/doi.org\/10.1109\/LCOMM.2018.2841832","journal-title":"IEEE Commun. Lett."},{"issue":"7","key":"30_CR16","doi-asserted-by":"publisher","first-page":"1573","DOI":"10.1109\/LCOMM.2022.3167813","volume":"26","author":"L Zhang","year":"2022","unstructured":"Zhang, L., Zhang, C., Shihada, B.: Efficient wireless traffic prediction at the edge: a federated meta-learning approach. IEEE Commun. Lett. 26(7), 1573\u20131577 (2022). https:\/\/doi.org\/10.1109\/LCOMM.2022.3167813","journal-title":"IEEE Commun. Lett."},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"30_CR18","doi-asserted-by":"publisher","unstructured":"Zhu, Y., Wang, S.: Joint traffic prediction and base station sleeping for energy saving in cellular networks. In: ICC 2021 - IEEE International Conference on Communications, pp.\u00a01\u20136 (2021). https:\/\/doi.org\/10.1109\/ICC42927.2021.9500442","DOI":"10.1109\/ICC42927.2021.9500442"}],"container-title":["Lecture Notes in Computer Science","Wireless Artificial Intelligent Computing Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-8725-1_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T14:23:23Z","timestamp":1750602203000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-8725-1_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819687244","9789819687251"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-8725-1_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"21 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WASA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Wireless Artificial Intelligent Computing Systems and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wasa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wasa-conference.org\/WASA2025\/index.html#","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}