{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T13:46:21Z","timestamp":1779371181387,"version":"3.53.1"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030109967","type":"print"},{"value":"9783030109974","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-10997-4_14","type":"book-chapter","created":{"date-parts":[[2019,1,17]],"date-time":"2019-01-17T12:30:23Z","timestamp":1547728223000},"page":"222-237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["ST-DenNetFus: A New Deep Learning Approach for Network Demand Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9475-0579","authenticated-orcid":false,"given":"Haytham","family":"Assem","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8491-4453","authenticated-orcid":false,"given":"Bora","family":"Caglayan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1806-2915","authenticated-orcid":false,"given":"Teodora Sandra","family":"Buda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1090-3548","authenticated-orcid":false,"given":"Declan","family":"O\u2019Sullivan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,1,18]]},"reference":[{"issue":"5","key":"14_CR1","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1109\/MWC.2013.6664479","volume":"20","author":"H Abou-Zeid","year":"2013","unstructured":"Abou-Zeid, H., Hassanein, H.S.: Predictive green wireless access: exploiting mobility and application information. IEEE Wirel. Commun. 20(5), 92\u201399 (2013)","journal-title":"IEEE Wirel. Commun."},{"issue":"5","key":"14_CR2","first-page":"70","volume":"8","author":"H Assem","year":"2017","unstructured":"Assem, H., Buda, T.S., O\u2019sullivan, D.: RCMC: recognizing crowd-mobility patterns in cities based on location based social networks data. ACM Trans. Intell. Syst. Technol. (TIST) 8(5), 70 (2017)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"14_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/978-3-319-71273-4_26","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"H Assem","year":"2017","unstructured":"Assem, H., Ghariba, S., Makrai, G., Johnston, P., Gill, L., Pilla, F.: Urban water flow and water level prediction based on deep learning. In: Altun, Y., et al. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10536, pp. 317\u2013329. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71273-4_26"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Assem, H., O\u2019Sullivan, D.: Discovering new socio-demographic regional patterns in cities. In: Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, p. 1. ACM (2016)","DOI":"10.1145\/3021304.3021305"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Assem, H., Xu, L., Buda, T.S., O\u2019Sullivan, D.: Spatio-temporal clustering approach for detecting functional regions in cities. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 370\u2013377. IEEE (2016)","DOI":"10.1109\/ICTAI.2016.0063"},{"issue":"3","key":"14_CR6","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)?-Arguments against avoiding RMSE in the literature. Geoscientific Model Dev. 7(3), 1247\u20131250 (2014)","journal-title":"Geoscientific Model Dev."},{"key":"14_CR7","unstructured":"Chollet, F.: Deep learning library for python. Runs on TensorFlow, Theano or CNTK. https:\/\/github.com\/fchollet\/keras. Accessed 09 Aug 2017"},{"key":"14_CR8","unstructured":"Cisco, I.: Cisco visual networking index: forecast and methodology, 2011\u20132016. CISCO White paper, pp. 2011\u20132016 (2012)"},{"key":"14_CR9","first-page":"014","volume":"4","author":"X Dong","year":"2015","unstructured":"Dong, X., Fan, W., Gu, J.: Predicting lte throughput using traffic time series. ZTE Commun. 4, 014 (2015)","journal-title":"ZTE Commun."},{"issue":"4","key":"14_CR10","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1109\/SURV.2011.092311.00031","volume":"13","author":"Z Hasan","year":"2011","unstructured":"Hasan, Z., Boostanimehr, H., Bhargava, V.K.: Green cellular networks: a survey, some research issues and challenges. IEEE Commun. Surv. Tutorials 13(4), 524\u2013540 (2011)","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"8","key":"14_CR12","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. arXiv preprint arXiv:1608.06993 (2016)","DOI":"10.1109\/CVPR.2017.243"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Jain, V., et al.: Supervised learning of image restoration with convolutional networks. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1\u20138. IEEE (2007)","DOI":"10.1109\/ICCV.2007.4408909"},{"issue":"2","key":"14_CR15","first-page":"202","volume":"17","author":"LU Khan","year":"2017","unstructured":"Khan, L.U.: Performance comparison of prediction techniques for 3G cellular traffic. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 17(2), 202 (2017)","journal-title":"Int. J. Comput. Sci. Netw. Secur. (IJCSNS)"},{"key":"14_CR16","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"14_CR17","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"issue":"7553","key":"14_CR18","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"14_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/978-3-642-35289-8_3","volume-title":"Neural Networks: Tricks of the Trade","author":"YA LeCun","year":"2012","unstructured":"LeCun, Y.A., Bottou, L., Orr, G.B., M\u00fcller, K.-R.: Efficient BackProp. In: Montavon, G., Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 9\u201348. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35289-8_3"},{"key":"14_CR20","volume-title":"Forecasting Methods and Applications","author":"S Makridakis","year":"2008","unstructured":"Makridakis, S., Wheelwright, S.C., Hyndman, R.J.: Forecasting Methods and Applications. Wiley, Hoboken (2008)"},{"key":"14_CR21","unstructured":"Mathieu, M., Couprie, C., LeCun, Y.: Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440 (2015)"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Mikolov, T., Karafi\u00e1t, M., Burget, L., Cernock\u1ef3, J., Khudanpur, S.: Recurrent neural network based language model. In: Interspeech, vol. 2, p. 3 (2010)","DOI":"10.21437\/Interspeech.2010-343"},{"issue":"1","key":"14_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","volume":"2","author":"MM Najafabadi","year":"2015","unstructured":"Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E.: Deep learning applications and challenges in big data analytics. J. Big Data 2(1), 1 (2015)","journal-title":"J. Big Data"},{"issue":"6","key":"14_CR24","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/MCOM.2011.5783985","volume":"49","author":"E Oh","year":"2011","unstructured":"Oh, E., Krishnamachari, B., Liu, X., Niu, Z.: Toward dynamic energy-efficient operation of cellular network infrastructure. IEEE Commun. Mag. 49(6), 56\u201361 (2011)","journal-title":"IEEE Commun. Mag."},{"issue":"5","key":"14_CR25","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1109\/TNN.2005.853437","volume":"16","author":"K Papagiannaki","year":"2005","unstructured":"Papagiannaki, K., Taft, N., Zhang, Z.L., Diot, C.: Long-term forecasting of internet backbone traffic. IEEE Trans. Neural Netw. 16(5), 1110\u20131124 (2005)","journal-title":"IEEE Trans. Neural Netw."},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Paul, U., Subramanian, A.P., Buddhikot, M.M., Das, S.R.: Understanding traffic dynamics in cellular data networks. In: 2011 Proceedings IEEE INFOCOM, pp. 882\u2013890. IEEE (2011)","DOI":"10.1109\/INFCOM.2011.5935313"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Sadek, N., Khotanzad, A.: Multi-scale high-speed network traffic prediction using k-factor Gegenbauer ARMA model. In: 2004 IEEE International Conference on Communications, vol. 4, pp. 2148\u20132152. IEEE (2004)","DOI":"10.1109\/ICC.2004.1312898"},{"key":"14_CR28","unstructured":"Sahu, A.: Survey of reasoning using neural networks. arXiv preprint arXiv:1702.06186 (2017)"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Sayeed, Z., Liao, Q., Faucher, D., Grinshpun, E., Sharma, S.: Cloud analytics for wireless metric prediction-framework and performance. In: 2015 IEEE 8th International Conference on Cloud Computing (CLOUD), pp. 995\u2013998. IEEE (2015)","DOI":"10.1109\/CLOUD.2015.135"},{"key":"14_CR30","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"14_CR32","unstructured":"Waber, A.: The seasonality of mobile device usage (2014). https:\/\/marketingland.com\/seasonality-mobile-device-usage-warmer-weather-tempers-tech-95937"},{"key":"14_CR33","unstructured":"Wu, J., Wei, S.: Time Series Analysis. Hunan Science and Technology Press, ChangSha (1989)"},{"issue":"4","key":"14_CR34","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/TMC.2011.97","volume":"11","author":"J Yao","year":"2012","unstructured":"Yao, J., Kanhere, S.S., Hassan, M.: Improving QoS in high-speed mobility using bandwidth maps. IEEE Trans. Mob. Comput. 11(4), 603\u2013617 (2012)","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"4","key":"14_CR35","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1109\/TST.2013.6574678","volume":"18","author":"Y Yu","year":"2013","unstructured":"Yu, Y., Song, M., Fu, Y., Song, J.: Traffic prediction in 3G mobile networks based on multifractal exploration. Tsinghua Sci. Technol. 18(4), 398\u2013405 (2013)","journal-title":"Tsinghua Sci. Technol."},{"key":"14_CR36","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"14_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI, pp. 1655\u20131661 (2017)","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"14_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., Qi, D., Li, R., Yi, X., Li, T.: Predicting citywide crowd flows using deep spatio-temporal residual networks. arXiv preprint arXiv:1701.02543 (2017)","DOI":"10.1016\/j.artint.2018.03.002"},{"key":"14_CR39","unstructured":"Zhou, B., He, D., Sun, Z., Ng, W.H.: Network traffic modeling and prediction with ARIMA\/GARCH. In: Proceedings of HET-NETs Conference, pp. 1\u201310 (2005)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-10997-4_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T01:37:43Z","timestamp":1705455463000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-10997-4_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030109967","9783030109974"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-10997-4_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"18 January 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ecmlpkdd2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"535","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"131","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}