{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:31:04Z","timestamp":1775068264950,"version":"3.50.1"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T00:00:00Z","timestamp":1638835200000},"content-version":"vor","delay-in-days":340,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Due to the inherent chaotic and fractal dynamics in the price series of Bitcoin, this paper proposes a two\u2010stage Bitcoin price prediction model by combining the advantage of variational mode decomposition (VMD) and technical analysis. VMD eliminates the noise signals and stochastic volatility in the price data by decomposing the data into variational mode functions, while technical analysis uses statistical trends obtained from past trading activity and price changes to construct technical indicators. The support vector regression (SVR) accepts input from a hybrid of technical indicators (TI) and reconstructed variational mode functions (rVMF). The model is trained, validated, and tested in a period characterized by unprecedented economic turmoil due to the COVID\u201019 pandemic, allowing the evaluation of the model in the presence of the pandemic. The constructed hybrid model outperforms the single SVR model that uses only TI and rVMF as features. The ability to predict a minute intraday Bitcoin price has a huge propensity to reduce investors\u2019 exposure to risk and provides better assurances of annualized returns.<\/jats:p>","DOI":"10.1155\/2021\/1767708","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T17:35:08Z","timestamp":1638898508000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Two\u2010Stage Hybrid Machine Learning Model for High\u2010Frequency Intraday Bitcoin Price Prediction Based on Technical Indicators, Variational Mode Decomposition, and Support Vector Regression"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2164-2339","authenticated-orcid":false,"given":"Samuel Asante","family":"Gyamerah","sequence":"first","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"e_1_2_7_1_2","volume-title":"Top 5 Cryptocurrencies by Market Cap","author":"Reif N.","year":"2019"},{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.3934\/qfe.2019.4.739"},{"key":"e_1_2_7_3_2","volume-title":"Wtf Is Going on in the Bitcoin Market?","author":"Gradwell P.","year":"2020"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.econlet.2016.09.019"},{"key":"e_1_2_7_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ribaf.2018.08.008"},{"key":"e_1_2_7_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2019.04.012"},{"key":"e_1_2_7_7_2","volume-title":"Modeling and Forecasting Regional Gdp in sweden Using Autoregressive Models","author":"Zhang H.","year":"2013"},{"key":"e_1_2_7_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2008.05.008"},{"key":"e_1_2_7_9_2","doi-asserted-by":"crossref","unstructured":"GargS.andAnupriya Autoregressive integrated moving average model based prediction of bitcoin close price Proceedings of the 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT) December 2018 Tirunelveli India IEEE 473\u2013478.","DOI":"10.1109\/ICSSIT.2018.8748423"},{"key":"e_1_2_7_10_2","doi-asserted-by":"publisher","DOI":"10.3844\/ajassp.2009.1509.1514"},{"key":"e_1_2_7_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/sym11050610"},{"key":"e_1_2_7_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2020.01.006"},{"key":"e_1_2_7_13_2","article-title":"Application of svr with improved ant colony optimization algorithms in exchange rate forecasting","volume":"38","author":"Hung W.-M.","year":"2009","journal-title":"Control and Cybernetics"},{"key":"e_1_2_7_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/en6041887"},{"key":"e_1_2_7_15_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8962717"},{"key":"e_1_2_7_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00592-x"},{"key":"e_1_2_7_17_2","doi-asserted-by":"publisher","DOI":"10.4314\/jfas.v9i3s.61"},{"key":"e_1_2_7_18_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8304903"},{"key":"e_1_2_7_19_2","doi-asserted-by":"publisher","DOI":"10.3934\/mbe.2020367"},{"key":"e_1_2_7_20_2","doi-asserted-by":"crossref","unstructured":"XianL. J. IsmailS. MustaphaA. Abd WahabM. H. andIdrusS. Z. S. Crude oil price forecasting using hybrid support vector machine 917 Proceedings of the International Conference on Technology Engineering and Sciences (ICTES) October 2020 Penang Malaysia IOP Publishing 012045.","DOI":"10.1088\/1757-899X\/917\/1\/012045"},{"key":"e_1_2_7_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.econlet.2015.02.029"},{"key":"e_1_2_7_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfds.2018.10.001"},{"key":"e_1_2_7_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113250"},{"key":"e_1_2_7_24_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.4917289"},{"key":"e_1_2_7_25_2","volume-title":"Technical Analysis from A to Z","author":"Achelis S. B.","year":"2001"},{"key":"e_1_2_7_26_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v036.i11"},{"key":"e_1_2_7_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2288675"},{"key":"e_1_2_7_28_2","doi-asserted-by":"publisher","DOI":"10.21595\/jve.2018.19479"},{"key":"e_1_2_7_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"e_1_2_7_30_2","first-page":"155","article-title":"Support vector regression machines","volume":"9","author":"Drucker H.","year":"1997","journal-title":"Advances in Neural Information Processing Systems"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/1767708.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/1767708.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/1767708","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T21:37:05Z","timestamp":1723239425000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/1767708"}},"subtitle":[],"editor":[{"given":"Ning","family":"Cai","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/1767708"],"URL":"https:\/\/doi.org\/10.1155\/2021\/1767708","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-07-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"1767708"}}