{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:23:01Z","timestamp":1743009781969,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030980177"},{"type":"electronic","value":"9783030980184"}],"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\/978-3-030-98018-4_26","type":"book-chapter","created":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T20:27:09Z","timestamp":1646339229000},"page":"317-328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Energy Price Volatility Using Hybrid Artificial Neural Networks with\u00a0GARCH-Type Models"],"prefix":"10.1007","author":[{"given":"Pichayakone","family":"Rakpho","sequence":"first","affiliation":[]},{"given":"Woraphon","family":"Yamaka","sequence":"additional","affiliation":[]},{"given":"Rungrapee","family":"Phadkantha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"issue":"3","key":"26_CR1","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/0304-4076(86)90063-1","volume":"31","author":"T Bollerslev","year":"1986","unstructured":"Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econometrics 31(3), 307\u2013327 (1986)","journal-title":"J. Econometrics"},{"key":"26_CR2","unstructured":"Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus) (2015). arXiv preprint arXiv:1511.07289"},{"issue":"6","key":"26_CR3","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1016\/j.eneco.2010.04.014","volume":"32","author":"CL Chang","year":"2010","unstructured":"Chang, C.L., McAleer, M., Tansuchat, R.: Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets. Energy Econ. 32(6), 1445\u20131455 (2010)","journal-title":"Energy Econ."},{"issue":"1","key":"26_CR4","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/S0927-5398(96)00011-4","volume":"4","author":"RG Donaldson","year":"1997","unstructured":"Donaldson, R.G., Kamstra, M.: An artificial neural network-GARCH model for international stock return volatility. J. Empir. Financ. 4(1), 17\u201346 (1997)","journal-title":"J. Empir. Financ."},{"issue":"5","key":"26_CR5","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.1111\/j.1540-6261.1993.tb05128.x","volume":"48","author":"LR Glosten","year":"1993","unstructured":"Glosten, L.R., Jagannathan, R., Runkle, D.E.: On the relation between the expected value and the volatility of the nominal excess return on stocks. J. Financ. 48(5), 1779\u20131801 (1993)","journal-title":"J. Financ."},{"key":"26_CR6","doi-asserted-by":"publisher","first-page":"207563","DOI":"10.1109\/ACCESS.2020.3038564","volume":"8","author":"R Liao","year":"2020","unstructured":"Liao, R., Yamaka, W., Sriboonchitta, S.: Exchange rate volatility forecasting by hybrid neural network Markov switching Beta-t-EGARCH. IEEE Access 8, 207563\u2013207574 (2020)","journal-title":"IEEE Access"},{"key":"26_CR7","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1016\/j.procs.2016.07.145","volume":"91","author":"X Lu","year":"2016","unstructured":"Lu, X., Que, D., Cao, G.: Volatility forecast based on the hybrid artificial neural network and GARCH-type models. Procedia Comput. Sci. 91, 1044\u20131049 (2016)","journal-title":"Procedia Comput. Sci."},{"issue":"5","key":"26_CR8","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1080\/02664763.2020.1748180","volume":"48","author":"P Maneejuk","year":"2021","unstructured":"Maneejuk, P., Yamaka, W.: Significance test for linear regression: how to test without P-values? J. Appl. Stat. 48(5), 827\u2013845 (2021)","journal-title":"J. Appl. Stat."},{"issue":"8","key":"26_CR9","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1080\/07474938.2016.1167994","volume":"37","author":"GG Martinet","year":"2018","unstructured":"Martinet, G.G., McAleer, M.: On the invertibility of EGARCH (p, q). Economet. Rev. 37(8), 824\u2013849 (2018)","journal-title":"Economet. Rev."},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Nelson, D.B.: Conditional heteroskedasticity in asset returns: A new approach. Econometrica: J. Econometric Soc. 347\u2013370 (1991)","DOI":"10.2307\/2938260"},{"key":"26_CR11","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-319-73150-6_17","volume-title":"Econometrics for Financial Applications","author":"V Nov\u00e1k","year":"2018","unstructured":"Nov\u00e1k, V.: Fuzzy vs. probabilistic techniques in time series analysis. In: Anh, L.H., Dong, L.S., Kreinovich, V., Thach, N.N. (eds.) ECONVN 2018. SCI, vol. 760, pp. 213\u2013234. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-73150-6_17"},{"key":"26_CR12","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/978-3-030-48853-6_32","volume-title":"Data Science for Financial Econometrics","author":"P Tarkhamtham","year":"2021","unstructured":"Tarkhamtham, P., Yamaka, W., Maneejuk, P.: Forecasting volatility of oil prices via google trend: LASSO approach. In: Ngoc Thach, N., Kreinovich, V., Trung, N.D. (eds.) Data Science for Financial Econometrics. SCI, vol. 898, pp. 459\u2013471. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-48853-6_32"},{"issue":"20","key":"26_CR13","doi-asserted-by":"publisher","first-page":"7245","DOI":"10.1016\/j.eswa.2015.04.058","volume":"42","author":"W Kristjanpoller","year":"2015","unstructured":"Kristjanpoller, W., Minutolo, M.C.: Gold price volatility: a forecasting approach using the artificial neural network-GARCH model. Expert Syst. Appl. 42(20), 7245\u20137251 (2015)","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"26_CR14","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1016\/j.eneco.2010.07.009","volume":"32","author":"Y Wei","year":"2010","unstructured":"Wei, Y., Wang, Y., Huang, D.: Forecasting crude oil market volatility: further evidence using GARCH-class models. Energy Econ. 32(6), 1477\u20131484 (2010)","journal-title":"Energy Econ."},{"issue":"9","key":"26_CR15","doi-asserted-by":"publisher","first-page":"3997","DOI":"10.3390\/app11093997","volume":"11","author":"W Yamaka","year":"2021","unstructured":"Yamaka, W., Phadkantha, R., Maneejuk, P.: A convex combination approach for artificial neural network of interval data. Appl. Sci. 11(9), 3997 (2021)","journal-title":"Appl. Sci."}],"container-title":["Lecture Notes in Computer Science","Integrated Uncertainty in Knowledge Modelling and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-98018-4_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:05:40Z","timestamp":1646957140000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-98018-4_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030980177","9783030980184"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-98018-4_26","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":"4 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IUKM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ishikawa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"18 March 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 March 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iukm2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.jaist.ac.jp\/IUKM\/IUKM2022\/","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":"Easy chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46","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":"30","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":"65% - 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":"3.1","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":"2.7","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)"}}]}}