Abstract
In dynamic financial time series prediction, neural network training based on short data sequences results to more accurate predictions as using lengthy historical data. Optimal training set size is determined theoretically and experimentally. To reduce generalization error we: a) perform dimensionality reduction by mapping input data into low dimensional space using the multilayer perceptron, b) train the single layer perceptron classifier with short sequences of low-dimensional input data series, c) each time initialize the perceptron with weight vector obtained after training with previous portion of the data sequence, d) make use of useful preceding historical information accumulated in the financial time series data by the early stopping procedure.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kuncheva, L.: Classifier ensembles for changing environments. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 1–15. Springer, Heidelberg (2004)
Raudys, S.: Survival of intelligent agents in changing environments. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 109–117. Springer, Heidelberg (2004)
Raudys, A., Long, J.A.: MLP based linear feature extraction for nonlinearly separable data. Pattern Analysis and Applications 4(4), 227–234 (2001)
Huang, W., Lai, K.K., Nakamori, Y., Wang, S.: Forecasting foreign exchange rates with artificial neural networks: a review. Int. J. of Inf. Technology & Decision Making 3(1), 145–165 (2004)
Yao, X.: Evolving ertificial neural networks. Proceedings of IEEE 87(9), 1423–1447 (1999)
Moody, J.: Economic Forecasting: Challenges and Neural Network Solutions. In: International Symposium on Artificial Neural Networks, Hsinchu, Taiwan (1995)
Kohzadi, N., Boyd, M., Kermanshahi, B., Kaastra, I.: A comparison of artificial neural network and time-series models for forecasting commodity prices. Neurocomputing 10, 169–181 (1996)
Feldsend, J.E., Singh, S.: Pareto evolutionary neural networks. IEEE Trans. Neural Networks 16(2), 338–353 (2005)
Duda, P.E., Hart, R.O., Stork, D.G.: Pattern Classification, vol. 2. Wiley, NY (2000)
Raudys, S.: Statistical and Neural Classifiers: An integrated approach to design. Springer, NY (2001)
Raudys, S., Amari, S.: Effect of initial values in simple perception. In: Proc. 1998 IEEE World Congress on Comput. Intelligence. IJCNN 1998, pp. 1530–1535. IEEE Press, Los Alamitos (1998)
Fama, E.F.: Efficient capital markets: A review of theory and empirical work. Journal of Finance 25, 383–417 (1970)
Skurichina, M., Raudys, S., Duin, R.P.W.: K-nearest neighbors directed noise injection in multilayer perceptron training. IEEE Trans. on Neural Networks 11, 504–511 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Raudys, S., Zliobaite, I. (2005). Prediction of Commodity Prices in Rapidly Changing Environments. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_17
Download citation
DOI: https://doi.org/10.1007/11551188_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
Online ISBN: 978-3-540-28758-2
eBook Packages: Computer ScienceComputer Science (R0)