{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:36:54Z","timestamp":1777631814153,"version":"3.51.4"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2020,7,4]],"date-time":"2020-07-04T00:00:00Z","timestamp":1593820800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,7,4]],"date-time":"2020-07-04T00:00:00Z","timestamp":1593820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100008982","name":"Qatar National Research Fund","doi-asserted-by":"publisher","award":["NPRP10-0125-170250"],"award-info":[{"award-number":["NPRP10-0125-170250"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s00521-020-05129-6","type":"journal-article","created":{"date-parts":[[2020,7,4]],"date-time":"2020-07-04T06:02:35Z","timestamp":1593842555000},"page":"22979-22993","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":107,"title":["Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach"],"prefix":"10.1007","volume":"37","author":[{"given":"Mohammed","family":"Mudassir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shada","family":"Bennbaia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3146-3502","authenticated-orcid":false,"given":"Devrim","family":"Unal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Hammoudeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,4]]},"reference":[{"key":"5129_CR1","unstructured":"Xu W, Cooper A (2017) Digital spillover: measuring the true impact of the digital economy. Huawei and Oxford Economics. https:\/\/www.huawei.com\/minisite\/gci\/en\/digital-spillover\/files\/gci-digital-spillover.pdf. Accessed 2 July 2020"},{"key":"5129_CR2","doi-asserted-by":"publisher","unstructured":"Oliver J (2013) Mastering blockchain distributed ledger technology, and smart contracts explained-packt publishing, vol 53. Packt Publishing Ltd, https:\/\/doi.org\/10.1017\/CBO9781107415324.004, arXiv:1011.1669v3","DOI":"10.1017\/CBO9781107415324.004"},{"issue":"1","key":"5129_CR3","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.icte.2019.07.002","volume":"6","author":"D Unal","year":"2020","unstructured":"Unal D, Hammoudeh M, Kiraz MS (2020) Policy specification and verification for blockchain and smart contracts in 5G networks. ICT Express 6(1):43\u201347. https:\/\/doi.org\/10.1016\/j.icte.2019.07.002","journal-title":"ICT Express"},{"key":"5129_CR4","unstructured":"Morris DZ (2017) Bitcoin hits a new record high, but stops short of \\$20,000 | Fortune. https:\/\/fortune.com\/2017\/12\/17\/bitcoin-record-high-short-of-20000\/"},{"key":"5129_CR5","unstructured":"Shukla S, Dave S (2020) Bitcoin beats coronavirus blues. https:\/\/economictimes.indiatimes.com\/markets\/stocks\/news\/bitcoin-beats-coronavirus-blues\/articleshow\/75049718.cms"},{"key":"5129_CR6","doi-asserted-by":"publisher","first-page":"121950","DOI":"10.1016\/j.physa.2019.121950","volume":"533","author":"L Liu","year":"2019","unstructured":"Liu L (2019) Are Bitcon returns predictable?: Evidence from technical indicators. Physica A: Stat Mech Appl 533:121950. https:\/\/doi.org\/10.1016\/j.physa.2019.121950","journal-title":"Physica A: Stat Mech Appl"},{"issue":"3","key":"5129_CR7","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.jfds.2018.10.001","volume":"5","author":"JZ Huang","year":"2018","unstructured":"Huang JZ, Huang W, Ni J (2018) Predicting Bitcoin returns using high-dimensional technical indicators. J Finance Data Sci 5(3):140\u2013155. https:\/\/doi.org\/10.1016\/j.jfds.2018.10.001","journal-title":"J Finance Data Sci"},{"key":"5129_CR8","doi-asserted-by":"publisher","first-page":"112395","DOI":"10.1016\/j.cam.2019.112395","volume":"365","author":"Z Chen","year":"2020","unstructured":"Chen Z, Li C, Sun W (2020) Bitcoin price prediction using machine learning: an approach to sample dimension engineering. J Comput Appl Math 365:112395","journal-title":"J Comput Appl Math"},{"key":"5129_CR9","doi-asserted-by":"publisher","first-page":"121727","DOI":"10.1016\/j.physa.2019.121727","volume":"531","author":"R Adcock","year":"2019","unstructured":"Adcock R, Gradojevic N (2019) Non-fundamental, non-parametric Bitcoin forecasting. Physica A: Stat Mech Appl 531:121727. https:\/\/doi.org\/10.1016\/j.physa.2019.121727","journal-title":"Physica A: Stat Mech Appl"},{"key":"5129_CR10","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/j.asoc.2018.11.038","volume":"75","author":"DC Mallqui","year":"2019","unstructured":"Mallqui DC, Fernandes RA (2019) Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques. Appl Soft Comput 75:596\u2013606","journal-title":"Appl Soft Comput"},{"key":"5129_CR11","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.econlet.2018.02.017","volume":"166","author":"A Urquhart","year":"2018","unstructured":"Urquhart A (2018) What causes the attention of Bitcoin? Econ Lett 166:40\u201344","journal-title":"Econ Lett"},{"key":"5129_CR12","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.frl.2019.03.031","volume":"30","author":"D Philippas","year":"2019","unstructured":"Philippas D, Rjiba H, Guesmi K, Goutte S (2019) Media attention and Bitcoin prices. Finance Res Lett 30:37\u201343","journal-title":"Finance Res Lett"},{"issue":"3","key":"5129_CR13","first-page":"1","volume":"1","author":"J Abraham","year":"2018","unstructured":"Abraham J, Higdon D, Nelson J, Ibarra J (2018) Cryptocurrency price prediction using tweet volumes and sentiment analysis. SMU Data Sci Rev 1(3):1","journal-title":"SMU Data Sci Rev"},{"issue":"3","key":"5129_CR14","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.jfds.2018.10.001","volume":"5","author":"JZ Huang","year":"2019","unstructured":"Huang JZ, Huang W, Ni J (2019) Predicting Bitcoin returns using high-dimensional technical indicators. J Finance Data Sci 5(3):140\u2013155","journal-title":"J Finance Data Sci"},{"key":"5129_CR15","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.econlet.2018.11.007","volume":"174","author":"D Shen","year":"2019","unstructured":"Shen D, Urquhart A, Wang P (2019) Does twitter predict Bitcoin? Econ Lett 174:118\u2013122","journal-title":"Econ Lett"},{"issue":"8","key":"5129_CR16","doi-asserted-by":"publisher","first-page":"e0161197","DOI":"10.1371\/journal.pone.0161197","volume":"11","author":"YB Kim","year":"2016","unstructured":"Kim YB, Kim JG, Kim W, Im JH, Kim TH, Kang SJ, Kim CH (2016) Predicting fluctuations in cryptocurrency transactions based on user comments and replies. PloS one 11(8):e0161197. https:\/\/doi.org\/10.1371\/journal.pone.0161197","journal-title":"PloS one"},{"key":"5129_CR17","doi-asserted-by":"crossref","unstructured":"Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice, 2nd edn. OTexts, Melbourne, Australia. https:\/\/otexts.com\/fpp2\/. Accessed 2 July 2020","DOI":"10.32614\/CRAN.package.fpp2"},{"issue":"11","key":"5129_CR18","doi-asserted-by":"publisher","first-page":"130","DOI":"10.22161\/ijaers.4.11.20","volume":"4","author":"NA Bakar","year":"2017","unstructured":"Bakar NA, Rosbi S (2017) Autoregressive integrated moving average (ARIMA) model for fore-casting cryptocurrency exchange rate in high volatility\nenvironment: A new insight of bitcoin transaction. Int J Adv Eng Res Sci 4(11):130\u2013137. https:\/\/doi.org\/10.22161\/ijaers.4.11.20","journal-title":"Int J Adv Eng Res Sci"},{"issue":"2","key":"5129_CR19","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1109\/TPWRS.2005.846044","volume":"20","author":"RC Garcia","year":"2005","unstructured":"Garcia RC, Contreras J, Van Akkeren M, Garcia JBC (2005) A GARCH forecasting model to predict day-ahead electricity prices. IEEE Trans Power Syst 20(2):867\u2013874","journal-title":"IEEE Trans Power Syst"},{"issue":"2","key":"5129_CR20","doi-asserted-by":"publisher","first-page":"103","DOI":"10.3390\/jrfm12020103","volume":"12","author":"ZH Munim","year":"2019","unstructured":"Munim ZH, Shakil MH, Alon I (2019) Next-day Bitcoin price forecast. J Risk Financ Manag 12(2):103. https:\/\/doi.org\/10.3390\/jrfm12020103","journal-title":"J Risk Financ Manag"},{"key":"5129_CR21","doi-asserted-by":"publisher","first-page":"5427","DOI":"10.1109\/ACCESS.2017.2779181","volume":"6","author":"H Jang","year":"2017","unstructured":"Jang H, Lee J (2017) An empirical study on modeling and prediction of Bitcoin prices with bayesian neural networks based on blockchain information. IEEE Access 6:5427\u20135437","journal-title":"IEEE Access"},{"key":"5129_CR22","doi-asserted-by":"crossref","unstructured":"Shah D, Zhang K (2014) Bayesian regression and Bitcoin. In: 2014 52nd annual Allerton conference on communication, control, and computing (Allerton), IEEE, pp 409\u2013414","DOI":"10.1109\/ALLERTON.2014.7028484"},{"key":"5129_CR23","unstructured":"Chen GH, Nikolov S, Shah D (2013) A latent source model for nonparametric time series classification. In: Advances in neural information processing systems, pp 1088\u20131096"},{"key":"5129_CR24","doi-asserted-by":"publisher","unstructured":"Greaves A, Au B (2015) Using the Bitcoin transaction graph to predict the price of Bitcoin. https:\/\/doi.org\/10.1109\/CEC.2010.5586007","DOI":"10.1109\/CEC.2010.5586007"},{"key":"5129_CR25","doi-asserted-by":"publisher","unstructured":"McNally S, Roche J, Caton S (2018) Predicting the price of Bitcoin using machine learning. In: Proceedings\u201426th Euromicro international conference on parallel, distributed, and network-based processing, PDP 2018 pp 339\u2013343. https:\/\/doi.org\/10.1109\/PDP2018.2018.00060","DOI":"10.1109\/PDP2018.2018.00060"},{"issue":"1","key":"5129_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2133360.2133363","volume":"6","author":"FT Liu","year":"2012","unstructured":"Liu FT, Ting KM, Zhou ZH (2012) Isolation-based anomaly detection. ACM Trans Knowl Discov Data 6(1):1\u201339. https:\/\/doi.org\/10.1145\/2133360.2133363","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"4","key":"5129_CR27","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/BF02551274","volume":"2","author":"G Cybenko","year":"1989","unstructured":"Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303\u2013314. https:\/\/doi.org\/10.1007\/BF02551274","journal-title":"Math Control Signals Syst"},{"key":"5129_CR28","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"5129_CR29","unstructured":"Hanin B, Sellke M (2017) Approximating continuous functions by ReLU nets of minimal width. arXiv:1710.11278"},{"issue":"4","key":"5129_CR30","doi-asserted-by":"publisher","first-page":"155014772091640","DOI":"10.1177\/1550147720916404","volume":"16","author":"H Patel","year":"2020","unstructured":"Patel H, Rajput DS, Reddy GT, Iwendi C, Bashir AK, Jo O (2020) A review on classification of imbalanced data for wireless sensor networks. Int J Distrib Sens Netw 16(4):1550147720916404. https:\/\/doi.org\/10.1177\/1550147720916404","journal-title":"Int J Distrib Sens Netw"},{"issue":"9","key":"5129_CR31","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1097\/JTO.0b013e3181ec173d","volume":"5","author":"JN Mandrekar","year":"2010","unstructured":"Mandrekar JN (2010) Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol 5(9):1315\u20131316","journal-title":"J Thorac Oncol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05129-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05129-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05129-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T06:02:58Z","timestamp":1759125778000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05129-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,4]]},"references-count":31,"journal-issue":{"issue":"28","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["5129"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05129-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,4]]},"assertion":[{"value":"15 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2020","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 interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Data available at:","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and material"}}]}}