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Abu-Mostafa, Y. S., & Atiya, A. F. Introduction to financial forecasting. Applied intelligence., 1996 6(3):205–213.Abu-MostafaY. S.AtiyaA. F.Introduction to financial forecasting199663205213Search in Google Scholar
Han, R., & Cao, Q. Fuzzy chance constrained least squares twin support vector machine for uncertain classification. Journal of Intelligent & Fuzzy Systems., 2017 33(5):3041–3049.HanR.CaoQ.Fuzzy chance constrained least squares twin support vector machine for uncertain classification201733530413049Search in Google Scholar
Hsieh, L. F., Hsieh, S. C., & Tai, P. H. Enhanced stock price variation prediction via DOE and BPNN-based optimization. Expert Systems with Applications., 2011 38(11):14178–14184.HsiehL. F.HsiehS. C.TaiP. H.Enhanced stock price variation prediction via DOE and BPNN-based optimization201138111417814184Search in Google Scholar
S. Ismail and A. Shabri and R. Samsudin, A hybrid model of self-organizing maps (som) and least square support vector machine (lssvm) for time-series forecasting, Expert Systems with Applications., 2011 38(8): 10574–10578.IsmailS.ShabriA.SamsudinR.A hybrid model of self-organizing maps (som) and least square support vector machine (lssvm) for time-series forecasting20113881057410578Search in Google Scholar
Abrahart, R. J., & See, L. Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrological processes., 2000 14(11–12):2157–2172.AbrahartR. J.SeeL.Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments20001411–1221572172Search in Google Scholar
Li, G., & Shi, J. On comparing three artificial neural networks for wind speed forecasting. Applied Energy., 2010 87(7):2313–2320.LiG.ShiJ.On comparing three artificial neural networks for wind speed forecasting201087723132320Search in Google Scholar
C.J. Lu and J.Y. Wu, An efficient cmac neural network for stock index forecasting, Expert Systems with Applications., 2011 38(12):15194–15201.LuC.J.WuJ.Y.An efficient cmac neural network for stock index forecasting201138121519415201Search in Google Scholar
J. Lu and Y. Bai, Applications of grnn based on particle swarm algorithm forecasting stock prices, Perception & Psychophysics., 2013 30(3):303–307.LuJ.BaiY.Applications of grnn based on particle swarm algorithm forecasting stock prices2013303303307Search in Google Scholar
K.R. Mller and A.J. Smola and G. Rätsch and B. Schölkopf and J. Kohlmorgen and V. Vapnik, Predicting time series with support vector machines, Advances in Kernel Methods-Support Vector Learning., 1999 20(2): 999–1004.MllerK.R.SmolaA.J.RätschG.SchölkopfB.KohlmorgenJ.VapnikV.Predicting time series with support vector machines19992029991004Search in Google Scholar
E.E. Osuna, Support vector machines: training and applications, A. I. Memo no. 1602, C. B. C. L. Paper., 1997 144(9):1308–16.OsunaE.E.Support vector machines: training and applicationsA. I. Memo no. 1602,19971449130816Search in Google Scholar
W. Pan, Performing stock price prediction use of hybrid model, Chinese Management Studies., 2010 4(1):77–86.PanW.Performing stock price prediction use of hybrid model2010417786Search in Google Scholar
R. Shan and H. Dai and J. Zhao and W. Liu, Forecasting study of Shanghai's and Shenzhen's stock markets using a hybrid forecast method, Communications in Statistics – Simulation and Computation., 2015 44(4):1066–1077.ShanR.DaiH.ZhaoJ.LiuW.Forecasting study of Shanghai's and Shenzhen's stock markets using a hybrid forecast method201544410661077Search in Google Scholar
B. Shirmohammadi and M. Vafakhah and V. Moosavi and A. Moghaddamnia, Application of several data-driven techniques for predicting groundwater level, Water Resources Management., 2013 27(2):419–432.ShirmohammadiB.VafakhahM.MoosaviV.MoghaddamniaA.Application of several data-driven techniques for predicting groundwater level2013272419432Search in Google Scholar
D.F. Specht, A general regression neural network, IEEE Trans Neural Netw., 1991 2(6):568–576.SpechtD.F.A general regression neural network199126568576Search in Google Scholar
F. Sun and C. Liang and L. Zhang and P. Lee and Z. Lin and J. Cao, Optimal partition algorithm of the rbf neural network and its application to nancial time series forecasting, Neural Computing & Applications., 2005 14(1): 36–44.SunF.LiangC.ZhangL.LeeP.LinZ.CaoJ.Optimal partition algorithm of the rbf neural network and its application to nancial time series forecasting20051413644Search in Google Scholar
T.Z. Tan and Q. Chai and G.S. Ng, Biological brain-inspired genetic complementary learning for stock market and bank failure prediction1, Computational Intelligence., 2007 23(2):236C261.TanT.Z.ChaiQ.NgG.S.Biological brain-inspired genetic complementary learning for stock market and bank failure prediction12007232236C261Search in Google Scholar
Wang, J. Z., Wang, J. J., Zhang, Z. G., & Guo, S. P. Forecasting stock indices with back propagation neural network. Expert Systems with Applications., 2011 38(11):14346–14355.WangJ. Z.WangJ. J.ZhangZ. G.GuoS. P.Forecasting stock indices with back propagation neural network201138111434614355Search in Google Scholar
L. Wang and Y. Zeng and T. Chen, Back propagation neural network with adaptive differential evolution algorithm for time series forecasting, Expert Systems with Applications., 2015 42(2):855–863.WangL.ZengY.ChenT.Back propagation neural network with adaptive differential evolution algorithm for time series forecasting2015422855863Search in Google Scholar