{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T18:01:28Z","timestamp":1767981688010,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2019,5,6]],"date-time":"2019-05-06T00:00:00Z","timestamp":1557100800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,5,6]],"date-time":"2019-05-06T00:00:00Z","timestamp":1557100800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"University \"Lucian Blaga\" of Sibiu","award":["research grants LBUS-IRG-2015-01"],"award-info":[{"award-number":["research grants LBUS-IRG-2015-01"]}]},{"name":"University \"Lucian Blaga\" of Sibiu","award":["research grants LBUS-IRG-2015-01"],"award-info":[{"award-number":["research grants LBUS-IRG-2015-01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s00521-019-04216-7","type":"journal-article","created":{"date-parts":[[2019,5,7]],"date-time":"2019-05-07T15:31:18Z","timestamp":1557243078000},"page":"2383-2396","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Automatic optimized support vector regression for financial data prediction"],"prefix":"10.1007","volume":"32","author":[{"given":"Dana","family":"Simian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florin","family":"Stoica","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alina","family":"B\u0103rbulescu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,6]]},"reference":[{"key":"4216_CR1","volume-title":"Forecasting methods and applications","author":"S Makridakis","year":"1998","unstructured":"Makridakis S, Wheelwright SC, Hyndman RJ (1998) Forecasting methods and applications. Wiley, New York"},{"issue":"1","key":"4216_CR2","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","volume":"14","author":"G Zhang","year":"1998","unstructured":"Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14(1):35\u201362","journal-title":"Int J Forecast"},{"key":"4216_CR3","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"14","author":"AJ Smola","year":"2004","unstructured":"Smola AJ, Scholkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199\u2013222","journal-title":"Stat Comput"},{"key":"4216_CR4","first-page":"155","volume":"9","author":"H Drucker","year":"1997","unstructured":"Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. Neural Inf Proc Syst 9:155\u2013161","journal-title":"Neural Inf Proc Syst"},{"issue":"11\u201312","key":"4216_CR5","doi-asserted-by":"publisher","first-page":"6105","DOI":"10.1016\/j.apm.2016.01.050","volume":"40","author":"F Kang","year":"2016","unstructured":"Kang F, Xu Q, Li J (2016) Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence. Appl Math Model 40(11\u201312):6105\u20136120","journal-title":"Appl Math Model"},{"key":"4216_CR6","doi-asserted-by":"publisher","unstructured":"Li Q, Jiang S (2004) Gene expression programming in prediction. In: WCICA 2004. Fifth world congress on control and automation. \n                  https:\/\/doi.org\/10.1109\/wcica.2004.1341971","DOI":"10.1109\/wcica.2004.1341971"},{"key":"4216_CR7","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489\u2013501","journal-title":"Neurocomputing"},{"issue":"10","key":"4216_CR8","doi-asserted-by":"publisher","first-page":"e1997","DOI":"10.1002\/stc.1997","volume":"24","author":"F Kang","year":"2017","unstructured":"Kang F, Liu J, Li J, Li S (2017) Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Struct Control Health 24(10):e1997","journal-title":"Struct Control Health"},{"key":"4216_CR9","doi-asserted-by":"crossref","unstructured":"Majhi R, Panda G, Sohoo S, Panda A, Choubey A (2008) Prediction of S&P 500 and DJIA stock indices using particle swarm optimization technique. In: Proc IEEE congress on evolutionary computation, pp 1276\u20131282","DOI":"10.1109\/CEC.2008.4630960"},{"key":"4216_CR10","doi-asserted-by":"publisher","unstructured":"Mehak U, Kamran R, Syed HA, Syed SAA (2016) Stock market prediction using machine techniques. In: Proceedings of the 3rd international conference on communications and information sciences. \n                  https:\/\/doi.org\/10.1109\/iccoins.2016.7783235","DOI":"10.1109\/iccoins.2016.7783235"},{"key":"4216_CR11","unstructured":"Allen DE, McAller M, Singh AK (2014) Machine nees and volatility: the don jones industrial average and the TRNA sentiment series. E-Prints Complutense. \n                  http:\/\/eprints.ucm.es\/24356\/\n                  \n                . Accessed 1 July 2017"},{"issue":"4","key":"4216_CR12","first-page":"327","volume":"9","author":"A B\u0103rbulescu","year":"2012","unstructured":"B\u0103rbulescu A, B\u0103utu E (2012) A Hybrid Approach for Modeling Financial Time Series. Int Arab J Inf Techn 9(4):327\u2013335","journal-title":"Int Arab J Inf Techn"},{"key":"4216_CR13","doi-asserted-by":"publisher","unstructured":"Yao J, Poh H-L (1995) Forecasting the KLSE index using neural networks. In: Proceedings of ICNN\u201995, pp 1012\u20131017. \n                  https:\/\/doi.org\/10.1109\/icnn.1995.487559","DOI":"10.1109\/icnn.1995.487559"},{"issue":"1","key":"4216_CR14","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1504\/IJEF.2006.008837","volume":"1","author":"W-H Chen","year":"2006","unstructured":"Chen W-H, Shih J-Y (2006) Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets. Int J Electron Fin 1(1):49\u201367","journal-title":"Int J Electron Fin"},{"issue":"11","key":"4216_CR15","first-page":"8","volume":"92","author":"D Chandwani","year":"2014","unstructured":"Chandwani D, Saluja MS (2014) Stock direction forecasting techniques: an empirical study combining machine learning system with market indicators in the indian context. Int J Comput Appl 92(11):8\u201317","journal-title":"Int J Comput Appl"},{"issue":"3","key":"4216_CR16","first-page":"689","volume":"2","author":"R Choudhry","year":"2008","unstructured":"Choudhry R, Garg K (2008) A Hybrid machine learning system for stock market forecasting. Eng Technol 2(3):689\u2013692","journal-title":"Eng Technol"},{"issue":"1","key":"4216_CR17","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/S1057-5219(99)00006-X","volume":"8","author":"MRE Shazly","year":"1999","unstructured":"Shazly MRE, Shazly HEE (1999) Forecasting currency prices using genetically evolved neural network architecture. Int Rev Financ Anal 8(1):67\u201382","journal-title":"Int Rev Financ Anal"},{"issue":"4","key":"4216_CR18","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/72.935093","volume":"12","author":"TV Gestel","year":"2001","unstructured":"Gestel TV et al (2001) Financial time-series prediction using least squares support vector machines within the evidence framework. IEEE T Neural Netw 12(4):809\u2013821","journal-title":"IEEE T Neural Netw"},{"issue":"5","key":"4216_CR19","first-page":"16","volume":"9","author":"A Tarsauliya","year":"2010","unstructured":"Tarsauliya A et al (2010) Analysis of Artificial Neural Network for Financial Time Series Forecasting. Int J Comput Appl 9(5):16\u201322","journal-title":"Int J Comput Appl"},{"issue":"2","key":"4216_CR20","first-page":"111","volume":"7","author":"H Mizuno","year":"1998","unstructured":"Mizuno H, Kosaka M, Yajima H (1998) Application of neural network to technical analysis of stock market prediction. Stud Inf Control 7(2):111\u2013120","journal-title":"Stud Inf Control"},{"issue":"6","key":"4216_CR21","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1109\/72.728395","volume":"9","author":"DC Wunsch","year":"1998","unstructured":"Wunsch DC, Saad EW, Prokhorov DV (1998) Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE T Neural Netw 9(6):1456\u20131460","journal-title":"IEEE T Neural Netw"},{"key":"4216_CR22","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.procs.2015.04.167","volume":"48","author":"I Khandelwal","year":"2015","unstructured":"Khandelwal I, Adhikari R, Verma G (2015) Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition. Proc Comput Sci 48:173\u2013179","journal-title":"Proc Comput Sci"},{"issue":"2","key":"4216_CR23","first-page":"23","volume":"3","author":"N Merh","year":"2010","unstructured":"Merh N, Saxena VP, Pardasani KR (2010) A comparison between hybrid approaches of ANN and ARIMA for Indian stock trend forecasting. Bus Intel J 3(2):23\u201343","journal-title":"Bus Intel J"},{"issue":"3","key":"4216_CR24","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/72.572107","volume":"8","author":"X Yao","year":"1997","unstructured":"Yao X, Liu Y (1997) A new evolutionary system for evolving artificial neural networks. IEEE T Neural Netw 8(3):694\u2013713","journal-title":"IEEE T Neural Netw"},{"key":"4216_CR25","doi-asserted-by":"publisher","unstructured":"Mayer HA, Schwaiger R (1999) Evolutionary and coevolutionary approaches to time series prediction using generalized multi-layer perceptrons. In: Proceedings of 1999 congress on evolutionary computation\u2014CEC99, vol 1, pp 275\u2013280. \n                  https:\/\/doi.org\/10.1109\/cec.1999.781936","DOI":"10.1109\/cec.1999.781936"},{"key":"4216_CR26","unstructured":"Flores J et al (2009) Financial time series forecasting using a hybrid neural-evolutive approach. In: Proceedings of 15th SIGEF international conferecne, Lugo, Spain, pp 547\u2013555"},{"issue":"1\u20132","key":"4216_CR27","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0925-2312(03)00372-2","volume":"55","author":"KJ Kim","year":"2003","unstructured":"Kim KJ (2003) Financial time series forecasting using support vector machines. Neurocomputing 55(1\u20132):307\u2013319","journal-title":"Neurocomputing"},{"key":"4216_CR28","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/s005210170010","volume":"10","author":"L Cao","year":"2001","unstructured":"Cao L, Tay FEH (2001) Financial forecasting using support vector machines. Neural Comput Appl 10:184\u2013192","journal-title":"Neural Comput Appl"},{"key":"4216_CR29","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/S0925-2312(02)00577-5","volume":"51","author":"L Cao","year":"2003","unstructured":"Cao L (2003) Support vector machines experts for time series forecasting. Neurocomputing 51:321\u2013339","journal-title":"Neurocomputing"},{"key":"4216_CR30","unstructured":"de Oliveira JFL, Ludermir TB (2014) Iterative ARIMA-Multiple Support Vector Regression models for long term time series prediction. In: ESANN 2014 proceedings, European symposium on artificial neural networks, computational intelligence and machine learning. Bruges, Belgium \n                  http:\/\/www.i6doc.com\/fr\/livre\/?GCOI=28001100432440"},{"key":"4216_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"1995","unstructured":"Vapnik V (1995) The nature of statistical learning theory. Springer, Berlin"},{"issue":"10","key":"4216_CR32","first-page":"203","volume":"11","author":"D Basak","year":"2007","unstructured":"Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neu Inf Pro Lett Rev 11(10):203\u2013224","journal-title":"Neu Inf Pro Lett Rev"},{"key":"4216_CR33","doi-asserted-by":"publisher","unstructured":"Huang Q, Mao J, Liu Y (2012) An improved grid search algorithm of SVR parameters optimization. In: 2012 IEEE 14th international conference on communication technology. \n                  https:\/\/doi.org\/10.1109\/icct.2012.6511415","DOI":"10.1109\/icct.2012.6511415"},{"key":"4216_CR34","unstructured":"Diosan L, Oltean M, Rogozan A, Pecuchet JP (2007) Improving SVM performance using a linear combination of kernels. In Proceedings of the ICANNGA07, LNCS, vol 4432, pp 218\u2013227"},{"key":"4216_CR35","first-page":"256","volume":"7116","author":"D Simian","year":"2012","unstructured":"Simian D, Stoica F (2012) A general frame for building optimal multiple SVM kernels. LNCS 7116:256\u2013263","journal-title":"LNCS"},{"key":"4216_CR36","first-page":"2211","volume":"12","author":"M Gonen","year":"2011","unstructured":"Gonen M, Alpayd\u0131n E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12:2211\u20132268","journal-title":"J Mach Learn Res"},{"key":"4216_CR37","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/11776420_14","volume":"4005","author":"HQ Minh","year":"2006","unstructured":"Minh HQ, Niyogi P, Yao Y (2006) Mercers theorem feature maps, and smoothing learning theory. Lect Notes Comput Sci 4005:154\u2013168","journal-title":"Lect Notes Comput Sci"},{"key":"4216_CR38","unstructured":"Stoica F, Cacovean LF (2009) Using genetic algorithms and simulation as decision support in marketing strategies and long-term production planning. In: Proceedings of 9th international conference on simulation, modelling and optimization (SMO \u201809), pp 435\u2013439"},{"issue":"1","key":"4216_CR39","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1162\/evco.1993.1.1.25","volume":"1","author":"H M\u00fchlenbein","year":"1994","unstructured":"M\u00fchlenbein H, Schlierkamp-Voosen D (1994) Predictive models for the breeder genetic algorithm. I Continuous parameter optimization. Evol Comput 1(1):25\u201349","journal-title":"Evol Comput"},{"issue":"1","key":"4216_CR40","doi-asserted-by":"publisher","first-page":"62","DOI":"10.15837\/ijccc.2014.1.60","volume":"9","author":"F Stoica","year":"2014","unstructured":"Stoica F, Gh Boitor C (2014) Using the Breeder genetic algorithm to optimize a multiple regression analysis model used in prediction of the mesiodistal width of unerupted teeth. Int J Comput Commun 9(1):62\u201370","journal-title":"Int J Comput Commun"},{"key":"4216_CR41","unstructured":"Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines. \n                  http:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm"},{"issue":"4","key":"4216_CR42","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.12785\/amis\/070404","volume":"7","author":"E Bautu","year":"2013","unstructured":"Bautu E, Barbulescu A (2013) Forecasting meteorological time series using soft computing methods: an empirical study. Appl Math Inf Sci 7(4):1297\u20131306","journal-title":"Appl Math Inf Sci"},{"key":"4216_CR43","unstructured":"Crone SF, Lessmann S, Pietsch S (2006) Parameter Sensitivity of Support Vector Regression and Neural Networks for Forecasting. In: Proceedings of international conference on data mining DMIN\u201906, pp 396\u2013402"},{"key":"4216_CR44","unstructured":"Hsu C-W, Chang C-C, Lin C-J (2016) A practical guide to support vector classification. \n                  https:\/\/www.csie.ntu.edu.tw\/cjlin\/papers\/guide\/guide.pdf"},{"key":"4216_CR45","unstructured":"Han S, Qubo C, Meng H (2012) Parameter selection in SVM with RBF kernel function. In: Proceedings of the world automation congress 2012, Mexico"},{"issue":"1\u20132","key":"4216_CR46","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s00704-009-0160-7","volume":"100","author":"A B\u0103rbulescu","year":"2010","unstructured":"B\u0103rbulescu A, B\u0103utu E (2010) Mathematical models of climate evolution in Dobrudja. Theor Appl Climatol 100(1\u20132):29\u201344","journal-title":"Theor Appl Climatol"},{"issue":"2","key":"4216_CR47","first-page":"87","volume":"13","author":"C Ferreira","year":"2001","unstructured":"Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Nonl Phen Compl Syst 13(2):87\u2013129","journal-title":"Nonl Phen Compl Syst"},{"key":"4216_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-32849-1_2","volume-title":"Gene expression programming: mathematical modeling by an artificial intelligence","author":"C Ferreira","year":"2006","unstructured":"Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Springer, Berlin"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04216-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04216-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04216-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T18:12:41Z","timestamp":1602267161000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04216-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,6]]},"references-count":48,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["4216"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04216-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,6]]},"assertion":[{"value":"18 July 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2019","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 interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}