{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T09:30:57Z","timestamp":1743154257147,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031138317"},{"type":"electronic","value":"9783031138324"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-13832-4_48","type":"book-chapter","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T11:32:22Z","timestamp":1660563142000},"page":"589-603","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Comparative Study of Autoregressive and Neural Network Models: Forecasting the GARCH Process"],"prefix":"10.1007","author":[{"given":"Firuz","family":"Kamalov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ikhlaas","family":"Gurrib","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sherif","family":"Moussa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amril","family":"Nazir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"key":"48_CR1","doi-asserted-by":"crossref","unstructured":"Absar, N., Uddin, N., Khandaker, M.U., Ullah, H.: The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases. Infect. Disease Model. 7(1), 170\u2013183 (2022)","DOI":"10.1016\/j.idm.2021.12.005"},{"key":"48_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-3-030-33778-0_11","volume-title":"DS 2019","author":"A Azari","year":"2019","unstructured":"Azari, A., Papapetrou, P., Denic, S., Peters, G.: Cellular traffic prediction and classification: a comparative evaluation of LSTM and ARIMA. In: Kralj Novak, P., \u0160muc, T., D\u017eeroski, S. (eds.) DS 2019. LNCS (LNAI), vol. 11828, pp. 129\u2013144. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33778-0_11"},{"key":"48_CR3","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"key":"48_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.rinp.2021.103817","volume":"21","author":"J Devraj","year":"2021","unstructured":"Devraj, J., et al.: Forecasting of COVID-19 cases using deep learning models: is it reliable and practically significant? Results Phys. 21, 103817 (2021)","journal-title":"Results Phys."},{"key":"48_CR5","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/j.promfg.2019.02.234","volume":"32","author":"CD Dmitru","year":"2019","unstructured":"Dmitru, C.D., Gligor, A.: Wind energy forecasting: a comparative study between a stochastic model (ARIMA) and a model based on neural network (FFANN). Procedia Manuf. 32, 410\u2013417 (2019)","journal-title":"Procedia Manuf."},{"key":"48_CR6","doi-asserted-by":"crossref","unstructured":"Elsheikh, A.H., et al.: Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia. Process Saf. Environ. Protect. (2021)","DOI":"10.1016\/j.psep.2020.10.048"},{"key":"48_CR7","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctv14jx6sm","volume-title":"Time Series Analysis","author":"JD Hamilton","year":"2020","unstructured":"Hamilton, J.D.: Time Series Analysis. Princeton University Press, Princeton (2020)"},{"issue":"2\u20134","key":"48_CR8","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/S0360-8352(02)00036-0","volume":"42","author":"SL Ho","year":"2002","unstructured":"Ho, S.L., Xie, M., Goh, T.N.: A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction. Comput. Ind. Eng. 42(2\u20134), 371\u2013375 (2002)","journal-title":"Comput. Ind. Eng."},{"issue":"23","key":"48_CR9","doi-asserted-by":"publisher","first-page":"17655","DOI":"10.1007\/s00521-020-04942-3","volume":"32","author":"F Kamalov","year":"2020","unstructured":"Kamalov, F.: Forecasting significant stock price changes using neural networks. Neural Comput. Appl. 32(23), 17655\u201317667 (2020). https:\/\/doi.org\/10.1007\/s00521-020-04942-3","journal-title":"Neural Comput. Appl."},{"key":"48_CR10","doi-asserted-by":"crossref","unstructured":"Kamalov, F., Smail, L., Gurrib, I.: Forecasting with deep learning: S&P 500 index. In: 2020 13th International Symposium on Computational Intelligence and Design (ISCID), pp. 422\u2013425. IEEE, December 2020","DOI":"10.1109\/ISCID51228.2020.00102"},{"issue":"5","key":"48_CR11","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1007\/s12553-021-00587-x","volume":"11","author":"F Kamalov","year":"2021","unstructured":"Kamalov, F., Thabtah, F.: Forecasting Covid-19: SARMA-ARCH approach. Heal. Technol. 11(5), 1139\u20131148 (2021). https:\/\/doi.org\/10.1007\/s12553-021-00587-x","journal-title":"Heal. Technol."},{"key":"48_CR12","doi-asserted-by":"crossref","unstructured":"Kamalov, F., Gurrib, I., Thabtah, F.: Autoregressive and neural network models: a comparative study with linearly lagged series. In: 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 175\u2013180. IEEE, September 2021","DOI":"10.1109\/3ICT53449.2021.9581812"},{"issue":"4","key":"48_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3411760","volume":"1","author":"M Kumar","year":"2020","unstructured":"Kumar, M., Gupta, S., Kumar, K., Sachdeva, M.: Spreading of COVID-19 in India, Italy, Japan, Spain, UK, US: a prediction using ARIMA and LSTM model. Digit. Gov. Res. Pract. 1(4), 1\u20139 (2020)","journal-title":"Digit. Gov. Res. Pract."},{"key":"48_CR14","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.resourpol.2015.03.004","volume":"45","author":"FS Lasheras","year":"2015","unstructured":"Lasheras, F.S., de Cos Juez, F.J., S\u00e1nchez, A.S., Krzemie\u0144, A., Ferna\u0144dez, P.R.: Forecasting the COMEX copper spot price by means of neural networks and ARIMA models. Resour. Policy 45, 37\u201343 (2015)","journal-title":"Resour. Policy"},{"issue":"7","key":"48_CR15","doi-asserted-by":"publisher","first-page":"699","DOI":"10.7763\/JOEBM.2015.V3.269","volume":"3","author":"H Mombeini","year":"2015","unstructured":"Mombeini, H., Yazdani-Chamzini, A.: Modeling gold price via artificial neural network. J. Econ. Bus. Manag. 3(7), 699\u2013703 (2015)","journal-title":"J. Econ. Bus. Manag."},{"key":"48_CR16","volume-title":"Neural Networks and Deep Learning","author":"MA Nielsen","year":"2015","unstructured":"Nielsen, M.A.: Neural Networks and Deep Learning, vol. 25. Determination Press, San Francisco (2015)"},{"issue":"11","key":"48_CR17","doi-asserted-by":"publisher","first-page":"3880","DOI":"10.3390\/app10113880","volume":"10","author":"V Papastefanopoulos","year":"2020","unstructured":"Papastefanopoulos, V., Linardatos, P., Kotsiantis, S.: Covid-19: a comparison of time series methods to forecast percentage of active cases per population. Appl. Sci. 10(11), 3880 (2020)","journal-title":"Appl. Sci."},{"key":"48_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13369-021-06526-2","volume":"47","author":"K Rajab","year":"2022","unstructured":"Rajab, K., Kamalov, F., Cherukuri, A.K.: Forecasting COVID-19: vector autoregression-based model. Arab. J. Sci. Eng. 47, 1\u201310 (2022). https:\/\/doi.org\/10.1007\/s13369-021-06526-2","journal-title":"Arab. J. Sci. Eng."},{"issue":"2","key":"48_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-022-01019-x","volume":"3","author":"MA Rguibi","year":"2022","unstructured":"Rguibi, M.A., Moussa, N., Madani, A., Aaroud, A., Zine-dine, K.: Forecasting Covid-19 transmission with ARIMA and LSTM techniques in Morocco. SN Comput. Sci. 3(2), 1\u201314 (2022). https:\/\/doi.org\/10.1007\/s42979-022-01019-x","journal-title":"SN Comput. Sci."},{"key":"48_CR20","doi-asserted-by":"crossref","unstructured":"Seabold, S., Perktold, J.: Statsmodels: econometric and statistical modeling with python. In: Proceedings of the 9th Python in Science Conference, vol. 57, p. 61, June 2010","DOI":"10.25080\/Majora-92bf1922-011"},{"key":"48_CR21","unstructured":"Sharma, S., Yadav, M.: Analyzing the robustness of ARIMA and neural networks as a predictive model of crude oil prices. Theor. Appl. Econ. 27(2(623), Summer), 289\u2013300 (2020)"},{"issue":"3","key":"48_CR22","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1109\/TCSS.2019.2914499","volume":"6","author":"Y Weng","year":"2019","unstructured":"Weng, Y., Wang, X., Hua, J., Wang, H., Kang, M., Wang, F.Y.: Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by web crawler. IEEE Trans. Comput. Soc. Syst. 6(3), 547\u2013553 (2019)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"2","key":"48_CR23","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/s10660-019-09362-7","volume":"21","author":"H Weytjens","year":"2019","unstructured":"Weytjens, H., Lohmann, E., Kleinsteuber, M.: Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet. Electron. Commer. Res. 21(2), 371\u2013391 (2019). https:\/\/doi.org\/10.1007\/s10660-019-09362-7","journal-title":"Electron. Commer. Res."},{"issue":"8","key":"48_CR24","doi-asserted-by":"publisher","first-page":"04020086","DOI":"10.1061\/JTEPBS.0000388","volume":"146","author":"R Yao","year":"2020","unstructured":"Yao, R., Zhang, W., Zhang, L.: Hybrid methods for short-term traffic flow prediction based on ARIMA-GARCH model and wavelet neural network. J. Transp. Eng. Part A Syst. 146(8), 04020086 (2020)","journal-title":"J. Transp. Eng. Part A Syst."}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Methodologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13832-4_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:11:14Z","timestamp":1710339074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13832-4_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031138317","9783031138324"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13832-4_48","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2022","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":"icic2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2022\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"IC-ICC-CN","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"449","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":"209","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":"0","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":"47% - 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":"2.5","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)"}}]}}