By Christian L. Dunis, Jason Laws, Patrick Naïm
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Of regression Sum squared resid. Log likelihood Durbin–Watson stat. 019754 Mean dependent var. D. dependent var. Akaike info. 6760εt−10 The restricted ARMA(10,10) model was retained for out-of-sample estimation. The performance of the strategy is evaluated in terms of traditional forecasting accuracy and in terms of trading performance. Several other models were produced and their performance evaluated, for example an alternative restricted ARMA(10,10) model was produced (equation arma16710). The decision to retain the original restricted ARMA(10,10) model is because it has signiﬁcantly better in-sample trading results than the alternative ARMA(10,10) model.
From this point the network has stopped learning the similarities between the training and test sets, and has started to learn meaningless differences, namely the noise within the training data. For good generalisation ability, training should stop when the test set error reaches its lowest point. e. that the network will become overtrained (Dunis and Huang, 2002; Mehta, 1995). An evaluation of the performance of the trained network is made on new examples not used in network selection, namely the validation set.
And D. L. Rubinfeld (1998), Econometric Models and Economic Forecasts, 4th edition, McGraw-Hill, New York. fr/previa). Refenes, A. N. and A. Zaidi (1993), “Managing Exchange Rate Prediction Strategies with Neural Networks”, in P. J. G. Lisboa and M. J. Taylor (eds), Techniques and Applications of Neural Networks, Ellis Horwood, Hemel Hempstead, pp. 109–116. Shapiro, A. F. (2000), “A Hitchhiker’s Guide to the Techniques of Adaptive Nonlinear Models”, Insurance, Mathematics and Economics, 26, 119–132.