{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:44:58Z","timestamp":1765356298634},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Commun."],"published-print":{"date-parts":[[2018,7,1]]},"DOI":"10.1587\/transcom.2017cqp0007","type":"journal-article","created":{"date-parts":[[2018,1,21]],"date-time":"2018-01-21T22:22:20Z","timestamp":1516573340000},"page":"1612-1624","source":"Crossref","is-referenced-by-count":16,"title":["HOAH: A Hybrid TCP Throughput Prediction with Autoregressive Model and Hidden Markov Model for Mobile Networks"],"prefix":"10.23919","volume":"E101.B","author":[{"given":"Bo","family":"WEI","sequence":"first","affiliation":[{"name":"Waseda University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenji","family":"KANAI","sequence":"additional","affiliation":[{"name":"Waseda University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wataru","family":"KAWAKAMI","sequence":"additional","affiliation":[{"name":"Waseda University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiro","family":"KATTO","sequence":"additional","affiliation":[{"name":"Waseda University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] X. Yin, A. Jindal, V. Sekar, and B. Sinopoli, \u201cA control-theoretic approach for dynamic adaptive video streaming over HTTP,\u201d ACM SIGCOMM Comput. Commun. Rev., vol.45, no.4, pp.325-338, 2015. 10.1145\/2829988.2787486","DOI":"10.1145\/2829988.2787486"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] X.K. Zou, J. Erman, V. Gopalakrishnan, E. Halepovic, R. Jana, X Jin, J. Rexford, and R.K. Sinha, \u201cCan accurate predictions improve video streaming in cellular networks?,\u201d Proc. 16th International Workshop on Mobile Computing Systems and Applications, ACM, 2015. 10.1145\/2699343.2699359","DOI":"10.1145\/2699343.2699359"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] K. Satoda, H. Yoshida, H. Ito, and K. Ozawa, \u201cAdaptive video pacing method based on the prediction of stochastic TCP throughput,\u201d Global Communications Conference (GLOBECOM), IEEE, 2012. 10.1109\/glocom.2012.6503400","DOI":"10.1109\/GLOCOM.2012.6503400"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] H. Yoshida, K. Satoda, and T. Murase, \u201cConstructing stochastic model of TCP throughput on basis of stationarity analysis,\u201d Global Communications Conference (GLOBECOM), IEEE, 2013. 10.1109\/glocom.2013.6831293","DOI":"10.1109\/GLOCOM.2013.6831293"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] J. Kua, G. Armitage, and P. Branch, \u201cA survey of rate adaptation techniques for dynamic adaptive streaming over HTTP,\u201d IEEE Commun. Surveys Tuts., vol.19, no.3, pp.1842-1866, 2017. 10.1109\/comst.2017.2685630","DOI":"10.1109\/COMST.2017.2685630"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] K. Miller, A. Al-Tamimi, and A. Wolisz, \u201cQoE-based low-delay live streaming using throughput predictions,\u201d ACM Trans. Multimedia Comput. Commun. Appl. (TOMM), vol.13, no.1, pp.1-24, 2016. 10.1145\/2990505","DOI":"10.1145\/2990505"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] X. Yin, V. Sekar, and B. Sinopoli, \u201cToward a principled framework to design dynamic adaptive streaming algorithms over HTTP,\u201d Proc. 13th ACM Workshop on Hot Topics in Networks, ACM, 2014. 10.1145\/2670518.2673877","DOI":"10.1145\/2670518.2673877"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] Q. He, C. Dovrolis, and M. Ammar, \u201cOn the predictability of large transfer TCP throughput,\u201d ACM SIGCOMM Comput. Commun. Rev., vol.35, no.4, pp.145-156, 2005. 10.1145\/1090191.1080110","DOI":"10.1145\/1090191.1080110"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] M. Mathis, J. Semke, and J. Mahdavi, \u201cThe macroscopic behavior of the TCP congestion avoidance algorithm,\u201d ACM SIGCOMM Comput. Commun. Rev., vol.27, no.3, pp.67-82, 1997. 10.1145\/263932.264023","DOI":"10.1145\/263932.264023"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] J. Padhye, V. Firoiu, D.F. Towsley, and J.F. Kurose, \u201cModeling TCP Reno performance: A simple model and its empirical validation,\u201d IEEE\/ACM Trans. Netw. (ToN), vol.8, no.2, pp.133-145, 2000. 10.1109\/90.842137","DOI":"10.1109\/90.842137"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] S. Floyd, M. Handley, J. Padhye, and J. Widmer, \u201cEquation-based congestion control for unicast applications,\u201d ACM SIGCOMM Comput. Commun. Rev., vol.30, no.4, pp.43-56, 2000. 10.1145\/347057.347397","DOI":"10.1145\/347057.347397"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] S. Vazhkudai, J.M. Schopf, and I. Foster, \u201cPredicting the performance of wide area data transfers,\u201d Parallel and Distributed Processing Symposium, Proceedings International, IPDPS 2002, Abstracts and CD-ROM, IEEE, 2001. 10.1109\/ipdps.2002.1015510","DOI":"10.1109\/IPDPS.2002.1015510"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] M. Swany and R. Wolski, \u201cMultivariate resource performance forecasting in the network weather service,\u201d Supercomputing, ACM\/IEEE 2002 Conference. IEEE, 2002. 10.1109\/sc.2002.10039","DOI":"10.1109\/SC.2002.10039"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] B. Wei, K. Kanai, and J. Katto, \u201cHistory-based throughput prediction with Hidden Markov Model in mobile networks,\u201d Proc. 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2016. 10.1109\/icmew.2016.7574683","DOI":"10.1109\/ICMEW.2016.7574683"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] A. Jahangiri and H.A. Rakha, \u201cApplying machine learning techniques to transportation mode recognition using mobile phone sensor data,\u201d IEEE Trans. Intell. Transp. Syst., vol.16, no.5, pp.2406-2417, 2015. 10.1109\/tits.2015.2405759","DOI":"10.1109\/TITS.2015.2405759"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] Y. Qiao, Y. Cheng, J. Yang, J. Liu, and N. Kato, \u201cA mobility analytical framework for big mobile data in densely populated area,\u201d IEEE Trans Veh. Technol., vol.66, no.2, pp.1443-1455, 2017. 10.1109\/tvt.2016.2553182","DOI":"10.1109\/TVT.2016.2553182"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] W. Kawakami, K. Kanai, B. Wei, and J. Katto, \u201cAccuracy evaluations of human moving pattern using communication quality based on machine learning,\u201d Proc. IEEE GCCE 2017, Oct. 2017. 10.1109\/gcce.2017.8229351","DOI":"10.1109\/GCCE.2017.8229351"},{"key":"18","unstructured":"[18] HSDPA, http:\/\/home.ifi.uio.no\/paalh\/dataset\/hsdpa-tcp-logs\/"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] S. Takenaka, K. Kanai, J. Katto, and T. Murase, \u201cGreen video delivery system using moving route navigation and playout buffer control,\u201d IEEE Consumer Communications and Networking Conference, IEEE, Jan. 2017. 10.1109\/ccnc.2017.8015365","DOI":"10.1109\/CCNC.2017.8015365"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] M.E. Ayadi, Moataz, M.S. Kamel, and F. Karray, \u201cSurvey on speech emotion recognition: Features, classification schemes, and databases,\u201d Pattern Recogn., vol.44, no.3, pp.572-587, 2011. 10.1016\/j.patcog.2010.09.020","DOI":"10.1016\/j.patcog.2010.09.020"},{"key":"21","unstructured":"[21] M.N. Ayyaz, I. Javed, and W. Mahmood, \u201cHandwritten Character Recognition Using Multiclass SVM Classification with Hybrid Feature Extraction,\u201d Pakistan Journal of Engineering and Applied Sciences, vol.10, pp.57-67, 2016."},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] U, Thissen, R. van Brakel, A.P. de Weijer, W.J. Melssen, and L.M.C. Buydens, \u201cUsing support vector machines for time series prediction,\u201d Chemometrics and Intelligent Laboratory Systems, vol.69, no.1, pp.35-49, 2003. 10.1016\/s0169-7439(03)00111-4","DOI":"10.1016\/S0169-7439(03)00111-4"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] N.I. Sapankevych and R. Sankar, \u201cTime series prediction using support vector machines: A survey,\u201d IEEE Comput. Intell. Mag., vol.4, no.2, pp.24-38, 2009. 10.1109\/mci.2009.932254","DOI":"10.1109\/MCI.2009.932254"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] A. Kampouraki, G. Manis, and C. Nikou, \u201cHeartbeat time series classification with support vector machines,\u201d IEEE Trans. Inf. Technol. Biomed., vol.13, no.4, pp.512-518, 2009. 10.1109\/titb.2008.2003323","DOI":"10.1109\/TITB.2008.2003323"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] L.E. Nieto-Barajas and F.A. Quintana, \u201cA Bayesian non-parametric dynamic AR model for multiple time series analysis,\u201d J. Time Ser. Anal., vol.37, no.5, pp.675-689, 2016. 10.1111\/jtsa.12182","DOI":"10.1111\/jtsa.12182"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] G. Inoussa, H. Peng, and J. Wu, \u201cNonlinear time series modeling and prediction using functional weights wavelet neural network-based state-dependent AR model,\u201d Neurocomputing, vol.86, pp.59-74, 2012. 10.1016\/j.neucom.2012.01.010","DOI":"10.1016\/j.neucom.2012.01.010"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] D.A. Dickey and W.A. Fuller, \u201cDistribution of the estimators for autoregressive time series with a unit root,\u201d J. Am. Stat. Assoc., vol.74, no.366, pp.427-431, June 1979. 10.2307\/2286348","DOI":"10.2307\/2286348"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] A.W. Gregory and B.E. Hansen, \u201cResidual-based tests for cointegration in models with regime shifts,\u201d J. Econom., vol.70, no.1, pp.99-126, 1996. 10.1016\/0304-4076(69)41685-7","DOI":"10.1016\/0304-4076(69)41685-7"},{"key":"29","unstructured":"[29] O.P. Chimobi, \u201cGovernment expenditure and national income: A causality test for Nigeria,\u201d European Journal of Economic and Political Studies, vol.2, no.2, pp.1-12, 2009."},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] S.V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction, John Wiley & Sons, 2008. 10.1002\/9780470740156.index","DOI":"10.1002\/9780470740156.index"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] M.S. Crouse, R.D. Nowak, and R.G. Baraniuk, \u201cWavelet-based statistical signal processing using hidden Markov models,\u201d IEEE Trans. Signal Process., vol.46, no.4, pp.886-902, 1998. 10.1109\/78.668544","DOI":"10.1109\/78.668544"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] D.A. Reynolds, \u201cSpeaker identification and verification using Gaussian mixture speaker models,\u201d Speech Commun., vol.17, no.1-2, pp.91-108, 1995. 10.1016\/0167-6393(95)00009-d","DOI":"10.1016\/0167-6393(95)00009-D"},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] Y. Huang, K.B. Englehart, B. Hudgins, and A.D. C. Chan, \u201cA Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses,\u201d IEEE Trans. Biomed. Eng., vol.52, no.11, pp.1801-1811, 2005. 10.1109\/tbme.2005.856295","DOI":"10.1109\/TBME.2005.856295"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] H. Li, G. Hua, Z. Lin, Jonathan, J. Brandt, and J. Yang, \u201cProbabilistic elastic matching for pose variant face verification,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2013. 10.1109\/cvpr.2013.449","DOI":"10.1109\/CVPR.2013.449"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[35] J. Ajmera, I. McCowan, and H. Bourlard, \u201cSpeech\/music segmentation using entropy and dynamism features in a HMM classification framework,\u201d Speech Commun., vol.40, no.3, pp.351-363, 2003. 10.1016\/s0167-6393(02)00087-0","DOI":"10.1016\/S0167-6393(02)00087-0"},{"key":"36","unstructured":"[36] ITEC DASH-JS, [online]: http:\/\/www-itec.uni-klu.ac.at\/dash\/?page_id=746"},{"key":"37","doi-asserted-by":"publisher","unstructured":"[37] D. Park, L.R. Rilett, and G. Han, \u201cSpectral basis neural networks for real-time travel time forecasting,\u201d Journal of Transportation Engineering, vol.125, no.6, pp.515-523, 1999. 10.1061\/(asce)0733-947x(1999)125:6(515)","DOI":"10.1061\/(ASCE)0733-947X(1999)125:6(515)"}],"container-title":["IEICE Transactions on Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E101.B\/7\/E101.B_2017CQP0007\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T15:01:32Z","timestamp":1704898892000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E101.B\/7\/E101.B_2017CQP0007\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,1]]},"references-count":37,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2018]]}},"URL":"https:\/\/doi.org\/10.1587\/transcom.2017cqp0007","relation":{},"ISSN":["0916-8516","1745-1345"],"issn-type":[{"value":"0916-8516","type":"print"},{"value":"1745-1345","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,1]]}}}