{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:00:59Z","timestamp":1780084859741,"version":"3.54.0"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2017,11,21]],"date-time":"2017-11-21T00:00:00Z","timestamp":1511222400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2019,8]]},"DOI":"10.1007\/s00521-017-3283-2","type":"journal-article","created":{"date-parts":[[2017,11,21]],"date-time":"2017-11-21T07:54:16Z","timestamp":1511250856000},"page":"3369-3384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Financial time series prediction using distributed machine learning techniques"],"prefix":"10.1007","volume":"31","author":[{"given":"Usha Manasi","family":"Mohapatra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Babita","family":"Majhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suresh Chandra","family":"Satapathy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,11,21]]},"reference":[{"issue":"3","key":"3283_CR1","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/0925-2312(95)00039-9","volume":"10","author":"I Kaastra","year":"1996","unstructured":"Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3):215\u2013236","journal-title":"Neurocomputing"},{"key":"3283_CR2","first-page":"2653","volume":"3","author":"TZ Tan","year":"2005","unstructured":"Tan TZ, Quek C, Ng GS (2005) Brain inspired genetic complimentary learning for stock market prediction. IEEE Congr Evol Comput 3:2653\u20132660","journal-title":"IEEE Congr Evol Comput"},{"key":"3283_CR3","unstructured":"Lin HS, Chen ML, Tong CC, Dai JW (2007) Using grey and RBFNN to Predict the net asset value of single nation Equity Funds\u2014a case study of Taiwan, US, and Japan. In: Proceedings of IEEE international conference on grey systems and intelligent services, pp 892\u2013897"},{"key":"3283_CR4","first-page":"3295","volume":"71","author":"L Yu","year":"2008","unstructured":"Yu L, Lai KK, Wang S (2008) Multistage RBF neural network ensemble learning for exchange rates forecasting. Neuro Comput 71:3295\u20133302","journal-title":"Neuro Comput"},{"issue":"6","key":"3283_CR5","doi-asserted-by":"publisher","first-page":"10097","DOI":"10.1016\/j.eswa.2009.01.012","volume":"36","author":"R Majhi","year":"2009","unstructured":"Majhi R, Panda G, Majhi B, Sahoo G (2009) Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques. Expert Syst Appl 36(6):10097\u201310104","journal-title":"Expert Syst Appl"},{"issue":"5","key":"3283_CR6","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/TCYB.2013.2259229","volume":"43","author":"D Li","year":"2013","unstructured":"Li D, Wang W, Ismail F (2013) Fuzzy neural network technique for system state forecasting. IEEE Trans Cybern 43(5):1484\u20131494","journal-title":"IEEE Trans Cybern"},{"key":"3283_CR7","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.ins.2014.05.006","volume":"280","author":"M Pulido","year":"2014","unstructured":"Pulido M, Melin P, Castillo O (2014) Particle swarm optimization of ensemble neural networks with fuzzy aggregation for time series prediction of the Mexican Stock Exchange. Inf Sci 280:188\u2013204","journal-title":"Inf Sci"},{"key":"3283_CR8","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.knosys.2014.06.009","volume":"67","author":"T Korol","year":"2014","unstructured":"Korol T (2014) A fuzzy logic model for forecasting exchange rates. Knowl-Based Syst 67:49\u201360","journal-title":"Knowl-Based Syst"},{"key":"3283_CR9","doi-asserted-by":"publisher","first-page":"6267","DOI":"10.1016\/j.eswa.2015.01.035","volume":"42","author":"L Wang","year":"2015","unstructured":"Wang L, Wang Z, Zhao S, Tan S (2015) Stock market trend prediction using dynamical Bayesian factor graph. Expert Syst Appl 42:6267\u20136275","journal-title":"Expert Syst Appl"},{"key":"3283_CR10","first-page":"243","volume":"167","author":"F Shen","year":"2015","unstructured":"Shen F, Chao J, Zhao J (2015) Forecasting exchange rate using deep belief networks and conjugate gradient method. Neuro Comput 167:243\u2013253","journal-title":"Neuro Comput"},{"key":"3283_CR11","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.jefas.2016.07.002","volume":"21","author":"AH Moghaddam","year":"2016","unstructured":"Moghaddam AH, Moghaddam MH, Esfandyari M (2016) Stock market index prediction using artificial neural network. J Econ Fin Admin Sci 21:89\u201393","journal-title":"J Econ Fin Admin Sci"},{"key":"3283_CR12","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.eswa.2016.05.033","volume":"61","author":"MW Hsu","year":"2016","unstructured":"Hsu MW, Lessmann S, Sung MC, Ma T, Johnson JEV (2016) Bridging the divide in financial market forecasting: machine learners versus financial economists. Expert Syst Appl 61:215\u2013234","journal-title":"Expert Syst Appl"},{"issue":"8","key":"3283_CR13","doi-asserted-by":"publisher","first-page":"4064","DOI":"10.1109\/TSP.2007.896034","volume":"55","author":"CG Lopes","year":"2007","unstructured":"Lopes CG, Sayed AH (2007) Incremental adaptive strategies over distributed networks. IEEE Trans Signal Process 55(8):4064\u20134077","journal-title":"IEEE Trans Signal Process"},{"key":"3283_CR14","doi-asserted-by":"crossref","unstructured":"Johansson B, Keviczky T, Johansson M, Johansson KH (2008) Subgradient methods and consensus algorithms for solving convex optimization problems. In: IEEE conference on decision and control, pp 4185\u20134190","DOI":"10.1109\/CDC.2008.4739339"},{"key":"3283_CR15","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1016\/j.automatica.2007.09.003","volume":"44","author":"B Johansson","year":"2008","unstructured":"Johansson B, Speranzon A, Johansson M, Johansson KH (2008) On decentralized negotiation of optimal consensus. Automatica 44:1175\u20131179","journal-title":"Automatica"},{"issue":"6","key":"3283_CR16","doi-asserted-by":"publisher","first-page":"2365","DOI":"10.1109\/TSP.2009.2016226","volume":"57","author":"ID Schizas","year":"2009","unstructured":"Schizas ID, Mateos G, Giannakis GB (2009) Distributed LMS for consensus-based in-network adaptive processing. IEEE Trans Signal Process 57(6):2365\u20132382","journal-title":"IEEE Trans Signal Process"},{"issue":"9","key":"3283_CR17","doi-asserted-by":"publisher","first-page":"2069","DOI":"10.1109\/TAC.2010.2042987","volume":"55","author":"FS Cattivelli","year":"2010","unstructured":"Cattivelli FS, Sayed AH (2010) Diffusion strategies for distributed Kalman filtering and smoothing. IEEE Trans Autom Control 55(9):2069\u20132084","journal-title":"IEEE Trans Autom Control"},{"issue":"3","key":"3283_CR18","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1109\/TSP.2009.2033729","volume":"58","author":"FS Cattivelli","year":"2010","unstructured":"Cattivelli FS, Sayed AH (2010) Diffusion LMS strategies for distributed estimation. IEEE Trans Signal Process 58(3):1035\u20131048","journal-title":"IEEE Trans Signal Process"},{"issue":"4","key":"3283_CR19","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1109\/TIE.2016.2636119","volume":"64","author":"X Wang","year":"2016","unstructured":"Wang X, Mou S, Sun D (2016) Improvement of a distributed algorithm for solving linear equations. IEEE Trans Ind Electron 64(4):3113\u20133117","journal-title":"IEEE Trans Ind Electron"},{"key":"3283_CR20","doi-asserted-by":"publisher","first-page":"5908","DOI":"10.1109\/ACCESS.2016.2572303","volume":"4","author":"Z Zhao","year":"2016","unstructured":"Zhao Z, Feng J, Peng B (2016) A green distributed signal reconstruction algorithm in wireless sensor networks. Special section on green communications and networking for 5\u00a0g wireless. IEEE Access 4:5908\u20135917","journal-title":"IEEE Access"},{"issue":"14","key":"3283_CR21","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.1049\/el.2016.1190","volume":"52","author":"Zhao H Yu","year":"2016","unstructured":"Yu Zhao H (2016) Incremental M-estimate-based least-mean algorithm over distributed network. Electron Lett 52(14):1270\u20131272","journal-title":"Electron Lett"},{"key":"3283_CR22","doi-asserted-by":"crossref","unstructured":"Qin J, Fu W, Gao H, Zheng WX (2017) Distributed k-means algorithm and fuzzy c-means algorithm for sensor networks based on multi agent consensus theory. IEEE Trans Cybern 47(3):772\u2013783","DOI":"10.1109\/TCYB.2016.2526683"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3283-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-017-3283-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3283-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,6]],"date-time":"2019-09-06T18:16:17Z","timestamp":1567793777000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-017-3283-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,21]]},"references-count":22,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["3283"],"URL":"https:\/\/doi.org\/10.1007\/s00521-017-3283-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,11,21]]},"assertion":[{"value":"16 August 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2017","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"}}]}}