Abstract
In this study, we propose a multistage neural network metalearning technique for financial time series predication. First of all, an interval sampling technique is used to generate different training subsets. Based on the different training subsets, the different neural network models with different training subsets are then trained to formulate different base models. Subsequently, to improve the efficiency of metalearning, the principal component analysis (PCA) technique is used as a pruning tool to generate an optimal set of base models. Finally, a neural-network-based metamodel can be produced by learning from the selected base models. For illustration, the proposed metalearning technique is applied to foreign exchange rate predication.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
McCulloch, W.S., Pitts, W.: A Logical Calculus of the Ideas Imminent in Nervous Activity. Bulletin and Mathematical Biophysics 5, 115–133 (1943)
Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Addison-Wesley, Reading (1989)
Tsaih, R., Hsu, Y., Lai, C.C.: Forecasting S&P 500 Stock Index Futures with a Hybrid AI System. Decision Support Systems 23, 161–174 (1998)
Yu, L., Lai, K.K., Wang, S.Y., Huang, W.: A Bias-Variance-Complexity Trade-off Framework for Complex System Modeling. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3980, pp. 518–527. Springer, Heidelberg (2006)
Chan, P., Stolfo, S.: Meta-learning for Multistrategy and Parallel Learning. In: Proceedings of the Second International Workshop on Multistrategy Learning, pp. 150–165 (1993)
White, H.: Connectionist Nonparametric Regression: Multilayer Feedforward Networks Can Learn Arbitrary Mappings. Neural Networks 3, 535–549 (1990)
Hornik, K., Stinchocombe, M., White, H.: Multilayer Feedforward Networks are Universal Approximators. Neural Networks 2, 359–366 (1989)
Broomhead, D.S., Lowe, D.: Multivariable Functional Interpolation and Adaptive Networks. Complex Systems 2, 321–355 (1988)
Mackay, D.J.C.: The Evidence Framework Applied to Classification Problems. Natural Computation 4, 720–736 (1992)
Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (1986)
Yu, L., Wang, S.Y., Lai, K.K.: A Novel Nonlinear Ensemble Forecasting Model Incorporating GLAR and ANN for Foreign Exchange Rates. Computers & Operations Research 32, 2523–2541 (2005)
Lai, K.K., Yu, L., Wang, S.Y., Huang, W.: A Novel Nonlinear Neural Network Ensemble Model for Financial Time Series Forecasting. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3991, pp. 790–793. Springer, Heidelberg (2006)
Lai, K.K., Yu, L., Wang, S.Y., Huang, W.: Hybridizing Exponential Smoothing and Neural Network for Financial Time Series Prediction. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3994, pp. 493–500. Springer, Heidelberg (2006)
Hansen, L.K., Salamon, P.: Neural Network Ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)
Benediktsson, J.A., Sveinsson, J.R., Ersoy, O.K., Swain, P.H.: Parallel Consensual Neural Networks. IEEE Transactions on Neural Networks 8, 54–64 (1997)
Yu, L., Wang, S.Y., Lai, K.K.: A Novel Adaptive Learning Algorithm for Stock Market Prediction. In: Deng, X., Du, D.-Z. (eds.) ISAAC 2005. LNCS, vol. 3827, pp. 443–452. Springer, Heidelberg (2005)
Yu, L., Wang, S.Y., Lai, K.K.: An Adaptive BP Algorithm with Optimal Learning Rates and Directional Error Correction for Foreign Exchange Market Trend Prediction. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 498–503. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lai, K.K., Yu, L., Huang, W., Wang, S. (2006). Multistage Neural Network Metalearning with Application to Foreign Exchange Rates Forecasting. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_32
Download citation
DOI: https://doi.org/10.1007/11925231_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
Online ISBN: 978-3-540-49058-6
eBook Packages: Computer ScienceComputer Science (R0)