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.
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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
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DOI: https://doi.org/10.1007/11551188_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
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