{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:14:19Z","timestamp":1771665259489,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319569031","type":"print"},{"value":"9783319569048","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,8,30]],"date-time":"2017-08-30T00:00:00Z","timestamp":1504051200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-56904-8_14","type":"book-chapter","created":{"date-parts":[[2017,8,29]],"date-time":"2017-08-29T08:22:49Z","timestamp":1503994969000},"page":"139-149","source":"Crossref","is-referenced-by-count":17,"title":["An Application of Internet Traffic Prediction with Deep Neural Network"],"prefix":"10.1007","author":[{"given":"Sanam","family":"Narejo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eros","family":"Pasero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,8,30]]},"reference":[{"key":"14_CR1","unstructured":"Feng, H., Shu, Y.: Study on network traffic prediction techniques. In: International Conference on Wireless Communications, Networking and Mobile Computing, vol. 2, pp. 1041\u20131044. IEEE (2005)"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Park, K., Willinger, W. (eds.): Self-similar Network Traffic and Performance Evaluation, pp. 94\u201395. Wiley, New York (2000)","DOI":"10.1002\/047120644X"},{"issue":"4","key":"14_CR3","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1109\/90.944338","volume":"9","author":"S Floyd","year":"2001","unstructured":"Floyd, S., Paxson, V.: Difficulties in simulating the internet. IEEE\/ACM Trans. Netw. (TON) 9(4), 392\u2013403 (2001)","journal-title":"IEEE\/ACM Trans. Netw. (TON)"},{"issue":"4","key":"14_CR4","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.physleta.2006.04.063","volume":"357","author":"P Shang","year":"2006","unstructured":"Shang, P., Li, X., Kamae, S.: Nonlinear analysis of traffic time series at different temporal scales. Phys. Lett. A 357(4), 314\u2013318 (2006)","journal-title":"Phys. Lett. A"},{"issue":"1","key":"14_CR5","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.chaos.2004.09.104","volume":"25","author":"P Shang","year":"2005","unstructured":"Shang, P., Li, X., Kamae, S.: Chaotic analysis of traffic time series. Chaos, Solitons Fractals 25(1), 121\u2013128 (2005)","journal-title":"Chaos, Solitons Fractals"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Romeu, P., Zamora-Mart\u00ednez, F., Botella-Rocamora, P., Pardo, J.: Time-series forecasting of indoor temperature using pre-trained deep neural networks. In: International Conference on Artificial Neural Networks, pp. 451\u2013458. Springer, Heidelberg (2013)","DOI":"10.1007\/978-3-642-40728-4_57"},{"key":"14_CR7","first-page":"153","volume":"19","author":"Y Bengio","year":"2007","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. Adv. Neural. Inf. Process. Syst. 19, 153 (2007)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"7","key":"14_CR8","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527\u20131554 (2006)","journal-title":"Neural Comput."},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, Z.L., Ribeiro, V.J., Moon, S., Diot, C.: Small-time scaling behaviors of Internet backbone traffic: an empirical study. In: Twenty-Second Annual Joint Conference of the IEEE Computer and Communications, vol. 3, pp. 1826\u20131836. IEEE (2003)","DOI":"10.1109\/INFCOM.2003.1209205"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"You, C., Chandra, K.: Time series models for internet data traffic. In: 1999 Conference on Local Computer Networks, LCN\u201999, pp. 164\u2013171. IEEE (1999)","DOI":"10.1109\/LCN.1999.802013"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Hasegawa, M., Wu, G., Mizuni, M.: Applications of nonlinear prediction methods to the internet traffic. In: Circuits and Systems, 2001. ISCAS 2001. The 2001 IEEE International Symposium on Vol. 3, pp. 169\u2013172. IEEE (2001)","DOI":"10.1109\/ISCAS.2001.921273"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Cortez, P., Rio, M., Rocha, M., Sousa, P.: Internet traffic forecasting using neural networks. In: International Joint Conference on Neural Network Proceedings, pp. 2635\u20132642. IEEE (2006)","DOI":"10.1109\/IJCNN.2006.247142"},{"issue":"5","key":"14_CR13","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1109\/TITS.2014.2311123","volume":"15","author":"W Huang","year":"2014","unstructured":"Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191\u20132201 (2014)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"14_CR14","unstructured":"Liu, J.N., Hu, Y., You, J.J., Chan, P.W.: Deep neural network based feature representation for weather forecasting. In: International Conference on Artificial Intelligence (ICAI), p. 1. WorldComp (2014)"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Dalto, M.: Deep neural networks for time series prediction with applications in ultra-short-term wind forecasting. In: IEEE ICIT (2015)","DOI":"10.1109\/ICIT.2015.7125335"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Oliveira, T.P., Barbar, J.S., Soares, A.S.: Multilayer Perceptron and Stacked Autoencoder for Internet Traffic Prediction. In: IFIP International Conference on Network and Parallel Computing, pp. 61\u201371. Springer, Heidelberg. (2014)","DOI":"10.1007\/978-3-662-44917-2_6"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Huang, W., Hong, H., Li, M., Hu, W., Song, G., Xie, K.: Deep architecture for traffic flow prediction. In: International Conference on Advanced Data Mining and Applications pp. 165\u2013176. Springer, Heidelberg (2013)","DOI":"10.1007\/978-3-642-53917-6_15"},{"key":"14_CR18","unstructured":"Larochelle, H., Bengio, Y., Louradour, J., Lamblin, P.: Exploring strategies for training deep neural networks. J. Mach. Learn. Res.pp. 1\u201340. (2009)"},{"issue":"1","key":"14_CR19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio, Y.: Learning deep architectures for AI. Foundations and trends\u00ae. Mach. Learn. 2(1), 1\u2013127 (2009)","journal-title":"Mach. Learn."}],"container-title":["Smart Innovation, Systems and Technologies","Multidisciplinary Approaches to Neural Computing"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-56904-8_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,2]],"date-time":"2019-10-02T21:48:58Z","timestamp":1570052938000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-56904-8_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,30]]},"ISBN":["9783319569031","9783319569048"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-56904-8_14","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"value":"2190-3018","type":"print"},{"value":"2190-3026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,30]]}}}