{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T17:50:37Z","timestamp":1767117037540,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030902407"},{"type":"electronic","value":"9783030902414"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-90241-4_25","type":"book-chapter","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T00:03:39Z","timestamp":1637107419000},"page":"318-332","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Analysis of Chaos and Predicting the Price of Crude Oil in Ecuador Using Deep Learning Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6518-5104","authenticated-orcid":false,"given":"Naomi","family":"Cede\u00f1o","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6680-4675","authenticated-orcid":false,"given":"G\u00e9nesis","family":"Carillo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2508-1885","authenticated-orcid":false,"given":"Mar\u00eda J.","family":"Ayala","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0480-0828","authenticated-orcid":false,"given":"Sebasti\u00e1n","family":"Lalvay","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8883-2730","authenticated-orcid":false,"given":"Saba","family":"Infante","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"key":"25_CR1","unstructured":"Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). http:\/\/tensorflow.org\/"},{"key":"25_CR2","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/S0378-4371(02)00985-8","volume":"313","author":"J Alvarez-Ramirez","year":"2002","unstructured":"Alvarez-Ramirez, J., Cisneros, M., Ibarra Valdez, C., Soriano, A.: Multifractal hurst analysis of crude oil prices. Phys. A Stat. Mech. Appl. 313, 651\u2013670 (2002)","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"25_CR3","doi-asserted-by":"publisher","first-page":"2540","DOI":"10.1016\/j.neucom.2010.06.004","volume":"73","author":"M Ardalani-Farsa","year":"2010","unstructured":"Ardalani-Farsa, M., Zolfaghari, S.: Chaotic time series prediction with residual analysis method using hybrid elman-narx neural networks. Neurocomputing 73, 2540\u20132553 (2010)","journal-title":"Neurocomputing"},{"key":"25_CR4","unstructured":"BCE: Sector Petrolero (2020). https:\/\/contenido.bce.fin.ec\/documentos\/Administracion\/bi_menuPetroleos.html#"},{"key":"25_CR5","doi-asserted-by":"publisher","first-page":"132261","DOI":"10.1016\/j.physd.2019.132261","volume":"403","author":"N Boull\u00e9","year":"2019","unstructured":"Boull\u00e9, N., Dallas, V., Nakatsukasa, Y., Samaddar, D.: Classification of chaotic time series with deep learning. Phys. D Nonlinear Phenom. 403, 132261 (2019)","journal-title":"Phys. D Nonlinear Phenom."},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Broock, W., Scheinkman, J., Dechert, W., Lebaron, B.: A test for independence based on the correlation dimension. Econometric Rev. 15, 197\u2013235 (1996)","DOI":"10.1080\/07474939608800353"},{"key":"25_CR7","doi-asserted-by":"publisher","first-page":"20120999","DOI":"10.1098\/rsif.2012.0999","volume":"10","author":"S Bruijn","year":"2013","unstructured":"Bruijn, S., Meijer, O., Beek, P., Van Dieen, J.: Assessing the stability of human locomotion: a review of current measures. J. R. Soc. Interface 10, 20120999 (2013)","journal-title":"J. R. Soc. Interface"},{"issue":"3","key":"25_CR8","first-page":"174","volume":"71","author":"X Chirivella","year":"2011","unstructured":"Chirivella, X., Ortega-Becea, J., Infante, S.: An\u00e1lisis no lineal de la frecuencia card\u00edaca fetal. Revista de obstetricia y ginecolog\u00eda de Venezuela 71(3), 174\u2013182 (2011)","journal-title":"Revista de obstetricia y ginecolog\u00eda de Venezuela"},{"key":"25_CR9","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.apenergy.2014.12.045","volume":"142","author":"H Chiroma","year":"2015","unstructured":"Chiroma, H., Abdulkareem, S., Herawan, T.: Evolutionary neural network model for west texas intermediate crude oil price prediction. Appl. Energ. 142, 266\u2013273 (2015)","journal-title":"Appl. Energ."},{"key":"25_CR10","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.eneco.2016.09.020","volume":"60","author":"K Drachal","year":"2016","unstructured":"Drachal, K.: Forecasting spot oil price in a dynamic model averaging framework: have the determinants changed over time? Energ. Econ. 60, 35\u201346 (2016)","journal-title":"Energ. Econ."},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Farmer, J., Sidorowich, J.: Exploiting chaos to predict the future and reduce noise. In: Evolution, Learning and Cognition, January 1988","DOI":"10.1142\/9789814434102_0011"},{"key":"25_CR12","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1103\/PhysRevA.33.1134","volume":"33","author":"A Fraser","year":"1986","unstructured":"Fraser, A., Swinney, H.: Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33, 1134\u20131140 (1986)","journal-title":"Phys. Rev. A"},{"key":"25_CR13","unstructured":"Garcia, M., Ruiz, J., Sanz, B.: El test BDS: Posibles limitaciones. Rect@ Actas 9 (2001)"},{"key":"25_CR14","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/0167-2789(83)90298-1","volume":"9","author":"P Grassberger","year":"1983","unstructured":"Grassberger, P., Procaccia, I.: Measuring strangeness strange attractors. Phys. D Nonlinear Phenom. 9, 189\u2013208 (1983)","journal-title":"Phys. D Nonlinear Phenom."},{"key":"25_CR15","volume-title":"A Guide to Chi-squared Testing","author":"P Greenwood","year":"1996","unstructured":"Greenwood, P., Nikulin, M.: A Guide to Chi-squared Testing, vol. 39. Wiley-Interscience, Hoboken (1996)"},{"key":"25_CR16","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1016\/j.procs.2020.03.136","volume":"170","author":"N Gupta","year":"2020","unstructured":"Gupta, N., Nigam, S.: Crude oil price prediction using artificial neural network. Procedia Comput. Sci. 170, 642\u2013647 (2020)","journal-title":"Procedia Comput. Sci."},{"key":"25_CR17","unstructured":"Hagan, M., Demuth, H., Beale, M.: Neural Network Design, vol. 2 pp. 2\u201314. Pws Pub, Boston (1996)"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"He, L.Y., Chen, S.P.: Are crude oil markets multifractal? evidence from MF-DFA and MF-SSA perspectives. Phys. A Stat. Mech. Appl. Phys. A 389, 3218\u20133229 (2010)","DOI":"10.1016\/j.physa.2010.04.007"},{"key":"25_CR19","first-page":"51","volume":"8","author":"S Infante","year":"2008","unstructured":"Infante, S., Ortega, J., Cede\u00f1o, F.: Estimaci\u00f3n de datos faltantes en estaciones meteorol\u00f3gicas de venezuela v\u00eda un modelo de redes neuronales. Rev. Climatol. 8, 51\u201370 (2008)","journal-title":"Rev. Climatol."},{"key":"25_CR20","doi-asserted-by":"publisher","first-page":"3403","DOI":"10.1103\/PhysRevA.45.3403","volume":"45","author":"M Kennel","year":"1992","unstructured":"Kennel, M., Brown, R., Abarbanel, H.: Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 45, 3403 (1992)","journal-title":"Phys. Rev. A"},{"key":"25_CR21","first-page":"1","volume":"16","author":"M Kuchaki Rafsanjani","year":"2016","unstructured":"Kuchaki Rafsanjani, M., Samareh, M.: Chaotic time series prediction by artificial neural networks. J. Comput. Methods Sci. Eng. 16, 1\u201317 (2016)","journal-title":"J. Comput. Methods Sci. Eng."},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Kugiumtzis, D., Bj\u00f8rn, L., Christophersen, N.: Chaotic time series part i: estimation of invariant properies in state space. Model. Identificat. Control 15 (1994)","DOI":"10.4173\/mic.1994.4.1"},{"key":"25_CR23","unstructured":"MATLAB: Neural net time series toolbox (2010). https:\/\/la.mathworks.com\/help\/deeplearning\/ref\/neuralnettimeseries-app.html"},{"key":"25_CR24","doi-asserted-by":"publisher","first-page":"3335","DOI":"10.1016\/j.neucom.2008.01.030","volume":"71","author":"JM Menezes Jr","year":"2008","unstructured":"Menezes, J.M., Jr., Barreto, G.: Long-term time series prediction with the narx network: an empirical evaluation. Neurocomputing 71, 3335\u20133343 (2008)","journal-title":"Neurocomputing"},{"key":"25_CR25","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1162\/neco.1989.1.2.281","volume":"1","author":"J Moody","year":"1989","unstructured":"Moody, J., Darken, C.: Fast learning in networks of locally-tuned processing units. Neural Comput. 1, 281\u2013294 (1989)","journal-title":"Neural Comput."},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Orojo, O., Tepper, J., Mcginnity, T., Mahmud, M.: A multi-recurrent network for crude oil price prediction (2020)","DOI":"10.1109\/SSCI44817.2019.9002841"},{"key":"25_CR27","doi-asserted-by":"publisher","unstructured":"Piorek, M.: Analysis of Chaotic Behavior in Non-linear Dynamical Systems Models and Algorithms for Quaternions. Springer International Publishing, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-319-94887-4","DOI":"10.1007\/978-3-319-94887-4"},{"key":"25_CR28","volume-title":"Deterministic Chaos: An Introduction","author":"H Schuster","year":"1984","unstructured":"Schuster, H.: Deterministic Chaos: An Introduction. Wiley-VCH Verlag, Weinheim (1984)"},{"key":"25_CR29","doi-asserted-by":"crossref","unstructured":"Shiblee, M., Kalra, P., Chandra, B.: Time series prediction with multilayer perceptron (mlp): a new generalized error based approach. In: Advances in Neuro-Information Processing: 15th International Conference, pp. 37\u201344, November 2008","DOI":"10.1007\/978-3-642-03040-6_5"},{"key":"25_CR30","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.eneco.2019.01.005","volume":"80","author":"V Singh","year":"2019","unstructured":"Singh, V., Kumar, P., Nishant, S.: Feedback spillover dynamics of crude oil and global assets indicators: a system-wide network perspective. Energ. Econ. 80, 321\u2013335 (2019)","journal-title":"Energ. Econ."},{"key":"25_CR31","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1111\/j.2517-6161.1992.tb01885.x","volume":"54","author":"R Smith","year":"1992","unstructured":"Smith, R.: Estimating dimensions in noisy chaotic time series. J. R. Stat. Soc. Ser. B (Methodol.) 54, 329\u2013351 (1992)","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"25_CR32","unstructured":"Trapletti, A., Hornik, K.: Tseries: time series analysis and computational finance (2020). https:\/\/CRAN.R-project.org\/package=tseries"},{"key":"25_CR33","doi-asserted-by":"crossref","unstructured":"Xiu, Y., Zhang, W.: Multivariate chaotic time series prediction based on narx neural networks. In: Proceedings of the 2nd International Conference on Electrical, Automation and Mechanical Engineering, pp. 164\u2013167 (2017)","DOI":"10.2991\/eame-17.2017.40"},{"key":"25_CR34","doi-asserted-by":"publisher","first-page":"5980","DOI":"10.3390\/su11215980","volume":"11","author":"T Yin","year":"2019","unstructured":"Yin, T., Wang, Y.: Predicting the price of WTI crude oil using ANN and chaos. Sustainability 11, 5980 (2019)","journal-title":"Sustainability"}],"container-title":["Communications in Computer and Information Science","Advanced Research in Technologies, Information, Innovation and Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-90241-4_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T09:48:58Z","timestamp":1726134538000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-90241-4_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030902407","9783030902414"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-90241-4_25","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"17 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ARTIIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"La Libertad","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ecuador","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"artiis2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.artiis.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"155","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":"53","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":"2","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":"34% - 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":"3","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)"}}]}}