{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T07:44:07Z","timestamp":1774511047060,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2018,9,26]],"date-time":"2018-09-26T00:00:00Z","timestamp":1537920000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Nature Science Foundation of China","award":["71532007"],"award-info":[{"award-number":["71532007"]}]},{"name":"Nature Science Foundation of China","award":["71131006"],"award-info":[{"award-number":["71131006"]}]},{"name":"Nature Science Foundation of China","award":["71172197"],"award-info":[{"award-number":["71172197"]}]},{"name":"Nature Science Foundation of China","award":["71673011"],"award-info":[{"award-number":["71673011"]}]},{"name":"Nature Science Foundation of China","award":["71273036"],"award-info":[{"award-number":["71273036"]}]},{"name":"Central University Fund of Sichuan University","award":["skgt201202"],"award-info":[{"award-number":["skgt201202"]}]},{"name":"Key Research and Development Plan of Science and Technology Department of Sichuan Province","award":["2017GZ0315"],"award-info":[{"award-number":["2017GZ0315"]}]},{"name":"Key Research and Development Plan of Science and Technology Department of Sichuan Province","award":["2017GZ0333"],"award-info":[{"award-number":["2017GZ0333"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2019,8]]},"DOI":"10.1007\/s12652-018-1059-x","type":"journal-article","created":{"date-parts":[[2018,9,26]],"date-time":"2018-09-26T13:24:55Z","timestamp":1537968295000},"page":"3315-3323","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["A hybrid ARIMA-SVR approach for forecasting emergency patient flow"],"prefix":"10.1007","volume":"10","author":[{"given":"Yumeng","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianchao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dunhu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruixiao","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yabing","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,26]]},"reference":[{"key":"1059_CR1","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.jbi.2015.06.022","volume":"57","author":"P Aboagye-Sarfo","year":"2015","unstructured":"Aboagye-Sarfo P, Mai Q, Sanfilippo FM, Preen DB, Stewart LM, Fatovich DM (2015) A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia. J Biomed Inform 57:62\u201373","journal-title":"J Biomed Inform"},{"issue":"3","key":"1059_CR2","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1109\/TITB.2009.2014565","volume":"13","author":"G Abraham","year":"2009","unstructured":"Abraham G, Byrnes GB, Bain CA (2009) Short-term forecasting of emergency inpatient flow. IEEE Trans Inf Technol Biomed 13(3):380\u2013383","journal-title":"IEEE Trans Inf Technol Biomed"},{"issue":"7","key":"1059_CR3","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s10916-016-0527-0","volume":"40","author":"M Afilal","year":"2016","unstructured":"Afilal M, Yalaoui F, Dugardin F, Amodeo L, Laplanche D, Blua P (2016) Forecasting the emergency department patients flow. J Med Syst 40(7):175","journal-title":"J Med Syst"},{"key":"1059_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-1014-x","author":"M Alafeef","year":"2018","unstructured":"Alafeef M, Fraiwan M (2018) On the diagnosis of idiopathic Parkinson\u2019s disease using continuous wavelet transform complex plot. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-018-1014-x","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1059_CR5","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.ijpe.2014.09.033","volume":"160","author":"GC Aye","year":"2015","unstructured":"Aye GC, Balcilar M, Gupta R, Majumdar A (2015) Forecasting aggregate retail sales: the case of South Africa. Int J Prod Econ 160:66\u201379","journal-title":"Int J Prod Econ"},{"issue":"2","key":"1059_CR6","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ienj.2013.08.001","volume":"22","author":"J Bergs","year":"2013","unstructured":"Bergs J, Heerinckx P, Verelst S (2013) Knowing what to expect, forecasting monthly emergency department visits: a time-series analysis. Int Emerg Nurs 22(2):112\u2013115","journal-title":"Int Emerg Nurs"},{"key":"1059_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-0960-7","author":"X Bi","year":"2018","unstructured":"Bi X, Ma H, Li JH, Ma YL, Chen DY (2018) A positive and unlabeled learning framework based on extreme learning machine for drug-drug interactions discovery. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-018-0960-7","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"2","key":"1059_CR8","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1057\/jors.1971.52","volume":"22","author":"GEP Box","year":"1971","unstructured":"Box GEP, Jenkins GM (1971) Time series analysis: forecasting and control. J Oper Res Soc 22(2):199\u2013201","journal-title":"J Oper Res Soc"},{"issue":"2","key":"1059_CR9","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1111\/j.1742-6723.2010.01273.x","volume":"22","author":"EW Chan","year":"2010","unstructured":"Chan EW, Taylor SE, Marriott J, Barger B (2010) An intervention to encourage ambulance paramedics to bring patients\u2019 own medications to the ED: impact on medications brought in and prescribing errors. Emerg Med Australas 22(2):151\u2013158","journal-title":"Emerg Med Australas"},{"issue":"10","key":"1059_CR10","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.1016\/j.enconman.2010.02.023","volume":"51","author":"JX Che","year":"2010","unstructured":"Che JX, Wang JZ (2010) Short-term electricity prices forecasting based on support vector regression and auto-regressive integrated moving average modeling. Energy Convers Manag 51(10):1911\u20131917","journal-title":"Energy Convers Manag"},{"issue":"8","key":"1059_CR11","doi-asserted-by":"publisher","first-page":"10368","DOI":"10.1016\/j.eswa.2011.02.049","volume":"38","author":"KY Chen","year":"2011","unstructured":"Chen KY (2011) Combining linear and nonlinear model in forecasting tourism demand. Expert Syst Appl 38(8):10368\u201310376","journal-title":"Expert Syst Appl"},{"issue":"4","key":"1059_CR12","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1007\/s12652-017-0504-6","volume":"9","author":"T Chen","year":"2018","unstructured":"Chen T (2018) An innovative fuzzy and artificial neural network approach for forecasting yield under an uncertain learning environment. J Ambient Intell Humaniz Comput 9(4):1013\u20131025. https:\/\/doi.org\/10.1007\/s12652-017-0504-6","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1059_CR13","doi-asserted-by":"publisher","DOI":"10.1111\/poms.12707","author":"RM Cui","year":"2017","unstructured":"Cui RM, Gallino S, Moreno A, Zhang DJ (2017) The operational value of social media information. Prod Oper Manag. https:\/\/doi.org\/10.1111\/poms.12707","journal-title":"Prod Oper Manag"},{"issue":"4","key":"1059_CR14","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1016\/j.annemergmed.2014.10.008","volume":"65","author":"A Ekstr\u00f6m","year":"2015","unstructured":"Ekstr\u00f6m A, Kurland L, Farrokhnia N, Castr\u00e9n M, Nordberg M (2015) Forecasting emergency department visits using Internet data. Ann Emerg Med 65(4):436\u2013442","journal-title":"Ann Emerg Med"},{"key":"1059_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-0801-8","author":"SS Ganesh","year":"2018","unstructured":"Ganesh SS, Arulmozhivarman P, Tatavarti VSNR (2018) Prediction of PM2.5 using an ensemble of artificial neural networks and regression models. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-018-0801-8","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1059_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-0882-4","author":"YZ Han","year":"2018","unstructured":"Han YZ, Deng Y (2018) A hybrid intelligent model for assessment of critical success factors in high-risk emergency system. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-018-0882-4","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"4","key":"1059_CR17","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1287\/opre.2015.1389","volume":"63","author":"JF Huang","year":"2015","unstructured":"Huang JF, Carmeli B, Mandelbaum A (2015) Control of patient flow in emergency departments, or multiclass queues with deadlines and feedback. Oper Res 63(4):892\u2013908","journal-title":"Oper Res"},{"issue":"8","key":"1059_CR18","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1111\/acem.12182","volume":"20","author":"M Izabel","year":"2013","unstructured":"Izabel M, Shakoor H, Nelson G (2013) Forecasting daily emergency department visits using calendar variables and ambient temperature readings. Acad Emerg Med 20(8):769\u2013777","journal-title":"Acad Emerg Med"},{"key":"1059_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-0896-y","author":"Y Jiang","year":"2018","unstructured":"Jiang Y, Zhang T, Gou Y, He LL, Bai HT, Hu CQ (2018) High-resolution temperature and salinity model analysis using support vector regression. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-018-0896-y","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1059_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-1005-y","author":"H Jin","year":"2018","unstructured":"Jin H, Wang HY, Gong C, Liu LX (2018) A study on the influencing factors of consumer information-seeking behavior in the context of ambient intelligence. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-018-1005-y","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"1","key":"1059_CR21","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.jbi.2008.05.003","volume":"42","author":"SS Jones","year":"2009","unstructured":"Jones SS, Evans RS, Allen TL, Thomas A, Haug PJ, Welch SJ, Snow GL (2009) A multivariate time series approach to modeling and forecasting demand in the emergency department. J Biomed Inform 42(1):123\u2013139","journal-title":"J Biomed Inform"},{"key":"1059_CR22","unstructured":"Krogh A, Vedelsby J (1994) Neural network ensembles, cross validation and active learning. In: International Conference on Neural Information Processing Systems, pp\u00a0231\u2013238"},{"issue":"1","key":"1059_CR23","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.knosys.2010.07.006","volume":"24","author":"YS Lee","year":"2011","unstructured":"Lee YS, Tong LI (2011) Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming. Knowl Based Syst 24(1):66\u201372","journal-title":"Knowl Based Syst"},{"key":"1059_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-0886-0","author":"ZL Liu","year":"2018","unstructured":"Liu ZL, Hajiali M, Torabi A, Ahmadi B, Simoes R (2018) Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-018-0886-0","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"1","key":"1059_CR25","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1186\/s12913-017-2407-9","volume":"17","author":"L Luo","year":"2017","unstructured":"Luo L, Luo L, Zhang XL, He XL (2017a) Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Serv Res 17(1):469","journal-title":"BMC Health Serv Res"},{"key":"1059_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s10729-017-9421-7","author":"L Luo","year":"2017","unstructured":"Luo L, Zhou Y, Han BT, Li JL (2017b) An optimization model to determine appointment scheduling window for an outpatient clinic with patient no-shows. Health Care Manag Sci. https:\/\/doi.org\/10.1007\/s10729-017-9421-7","journal-title":"Health Care Manag Sci"},{"key":"1059_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-0804-5","author":"S Niroomand","year":"2018","unstructured":"Niroomand S, Bazyar A, Alborzi M, Miami H, Mahmoodirad A (2018) A hybrid approach for multi-criteria emergency center location problem considering existing emergency centers with interval type data: a case study. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-018-0804-5","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1059_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-017-0655-5","author":"M Prabukumar","year":"2017","unstructured":"Prabukumar M, Agilandeeswari L, Ganesan K (2017) An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-017-0655-5","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1059_CR29","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.knosys.2017.03.027","volume":"125","author":"MJ Qin","year":"2017","unstructured":"Qin MJ, Li ZH, Du ZH (2017) Red tide time series forecasting by combining ARIMA and deep belief network. Knowl Based Syst 125:39\u201352","journal-title":"Knowl Based Syst"},{"key":"1059_CR30","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1016\/j.energy.2016.10.068","volume":"116","author":"P Sen","year":"2016","unstructured":"Sen P, Roy M, Pal P (2016) Application of ARIMA for forecasting energy consumption and GHG emission: a case study of an Indian pig iron manufacturing organization. Energy 116:1031\u20131038","journal-title":"Energy"},{"key":"1059_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"VN Vapnik","year":"1995","unstructured":"Vapnik VN (1995) The nature of statistical learning theory. Springer, New York"},{"key":"1059_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.asoc.2018.02.004","volume":"66","author":"L Wang","year":"2018","unstructured":"Wang L, Wang ZG, Qu H, Liu S (2018) Optimal forecast combination based on neural networks for time series forecasting. Appl Soft Comput 66:1\u201317","journal-title":"Appl Soft Comput"},{"issue":"6","key":"1059_CR33","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1136\/emj.2008.062380","volume":"26","author":"M Wargon","year":"2009","unstructured":"Wargon M, Guidet B, Hoang TD, Hejblum G (2009) A systematic review of models for forecasting the number of emergency department visits. Emerg Med J 26(6):395\u2013399","journal-title":"Emerg Med J"},{"issue":"9","key":"1059_CR34","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1111\/acem.12215","volume":"20","author":"JL Wiler","year":"2013","unstructured":"Wiler JL, Bolandifar E, Griffey RT, Poirier RF, Olsen T (2013) An emergency department patient flow model based on queueing theory principles. Acad Emerg Med 20(9):939\u2013946","journal-title":"Acad Emerg Med"},{"issue":"Supplement C","key":"1059_CR35","first-page":"157","volume":"110","author":"LJ Wu","year":"2016","unstructured":"Wu LJ, Cao GH (2016) Seasonal SVR with FOA algorithm for single-step and multi-step ahead forecasting in monthly inbound tourist flow. Knowl Based Syst 110(Supplement C):157\u2013166","journal-title":"Knowl Based Syst"},{"issue":"3","key":"1059_CR36","doi-asserted-by":"publisher","first-page":"4725","DOI":"10.1016\/j.eswa.2008.06.046","volume":"36","author":"CH Wu","year":"2009","unstructured":"Wu CH, Tzeng GH, Lin RH (2009) A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Syst Appl 36(3):4725\u20134735","journal-title":"Expert Syst Appl"},{"issue":"C","key":"1059_CR37","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.knosys.2015.01.002","volume":"77","author":"T Xiong","year":"2015","unstructured":"Xiong T, Li CG, Bao YK, Hu ZY, Zhang L (2015) A combination method for interval forecasting of agricultural commodity futures prices. Knowl Based Syst 77(C):92\u2013102","journal-title":"Knowl Based Syst"},{"issue":"3","key":"1059_CR38","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1016\/j.dss.2012.12.019","volume":"54","author":"M Xu","year":"2013","unstructured":"Xu M, Wong TC, Chin KS (2013) Modeling daily patient arrivals at emergency department and quantifying the relative importance of contributing variables using artificial neural network. Decis Support Syst 54(3):1488\u20131498","journal-title":"Decis Support Syst"},{"issue":"3","key":"1059_CR39","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1016\/j.dss.2012.12.006","volume":"54","author":"U Yolcu","year":"2013","unstructured":"Yolcu U, Egrioglu E, Aladag CH (2013) A new linear and nonlinear artificial neural network model for time series forecasting. Decis Support Syst 54(3):1340\u20131347","journal-title":"Decis Support Syst"},{"issue":"1","key":"1059_CR40","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","volume":"50","author":"GP Zhang","year":"2003","unstructured":"Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(1):159\u2013175","journal-title":"Neurocomputing"},{"issue":"2","key":"1059_CR41","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1109\/JBHI.2015.2511820","volume":"21","author":"T Zhu","year":"2017","unstructured":"Zhu T, Luo L, Zhang XL, Shi YK, Shen WW (2017) Time series approaches for forecasting the number of hospital daily discharged inpatients. IEEE J Biomed Health Inform 21(2):515\u2013526","journal-title":"IEEE J Biomed Health Inform"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-018-1059-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12652-018-1059-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-018-1059-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T19:53:32Z","timestamp":1720641212000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12652-018-1059-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,26]]},"references-count":41,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["1059"],"URL":"https:\/\/doi.org\/10.1007\/s12652-018-1059-x","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,26]]},"assertion":[{"value":"13 July 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}