Document Type : Research Paper

Author

Assistant Professor, Department of Mathematics and Computer Science, Allameh Tabataba’i University

Abstract

This paper proposes application of sliding window technique to time-delay neural network (TDNN) for prediction of financial time series. Neural network is a data-driven approach, in which we have huge data samples but limited information about the model structure. In this paper, we measure performance of the prediction and apply sliding window technique to select the most favorable neural network structure, time-delay taps and the most desirable training data size that result in the best prediction performance. The method was evaluated by using real data of share price of four firms traded in London Stock Exchange. The results show remarkable decrease for the root mean squared error, mean absolute percentage error and the linear regression of TDNN output offset.
 

Keywords

اصغری اسکویی، محمدرضا (1383)، «پیش بینی سری های زمانی با کمک شبکه عصبی»، پژوهش‌های اقتصادی ایران، فصلنامه علمی-پژوهشی مرکز تحقیقات اقتصاد ایران، سال شانزدهم، شماره 47، تابستان، صفحات 183-163.
اصغری اسکویی، محمدرضا (1392)، «هوش محاسباتی»، کرسی‌های علمی- ترویجی، دانشکده اقتصاد، دانشگاه علامه طباطبایی
Castillo, Oscar and Patricia Melin (2002), “Hybrid Intelligent Systems for Time Series Prediction Using Neural Networks, Fuzzy Logic and Fractal Theory”, Neural Networks, IEEE Transactions on, Vol.13, No.6, pp.1395,1408.
Chng, E.S., S. Chen and B. Mulgrew (1996) “Gradient Radial Basis Function Networks for Nonlinear and Nonstationary Time Series Prediction”, Neural Networks, IEEE Transactions on, Vol.7, No.1, 190-194.
Connor, J.T., R.D. Martin and L.E. Atlas (1994),“Recurrent Neural Networks and Robust Time Series Prediction”, Neural Networks, IEEE Transactions on , Vol.5, No.2, pp. 240-254.
Daniel Graves, Witold Pedrycz (2009), “Fuzzy Prediction Architecture Using Recurrent Neural Networks”, Neurocomputing, Vol. 72, No. 7–9, pp. 1668-1678.
De Gooijer, G. J. and R. J. Hyndman (2006), “25 Years of Time Series Forecasting”, International Journal of Forecasting, Vol. 22, No 3, pp. 443- 473.
Frank, R. J., N. Davey and S. P. Hunt (2001), “Time Series Prediction and Neural Networks”, Journal of Intelligent and Robotic Systems, Vol. 31, No. 1-3, pp. 91-103.
Hamzaçebi, Coşkun, Diyar Akay and Fevzi Kutay (2009), “Comparison of Direct and Iterative Artificial Neural Network Forecast Approaches in Multi-periodic Time Series Forecasting”, Expert Systems with Applications, Vol. 36, No. 2, pp. 3839-3844.
Lu, Chi-Jie, Tian-Shyug Lee and Chih-Chou Chiu (2009), Financial Time Series Forecasting Using Independent Component Analysis and Support Vector Regression, Decision Support Systems, Vol. 47, No. 2, pp. 115-125.
Knerr, C. (2004), “Time Series Prediction Using Neural Networks”, PhD Thesis, Texas Tech University.
Panagiotopoulos, A. (2012), “Optimizing Time Series Forecast Through Linear Programming”, PhD Thesis, Nottingham University.
Sapankevych, N. I. and Ravi Sankar (2009), “Time Series Prediction Using Support Vector Machines: A Survey”, Computational Intelligence Magazine, IEEE , Vol.4, No.2, pp. 24-38.
Van Gestel, T. and others (2001), “Financial Time Series Prediction Using Least Squares Support Vector Machines within the Evidence Framework”, Neural Networks, IEEE Transactions on , Vol.12, No.4, pp.809-821.
Zhang, Jun, H.S.H.Chung and Wai-Lun Lo (2008), “Chaotic Time Series Prediction Using a Neuro-Fuzzy System with Time-Delay Coordinates”, Knowledge and Data Engineering, IEEE Transactions on, Vol.20, No.7, pp. 956,964.