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University of Mauritius Research Journal

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Improving Artificial Neural Network Forecasts with Kalman Filtering

Jayrani Cheeneebash, George Galanis, Ashvin Gopaul, Muddun Bhuruth

Abstract


In this paper, we examine the use of the artificial neural network method as a forecasting technique in financial time series and the application of a Kalman filter algorithm to improve the accuracy of the model. Forecasting accuracy criteria are used to compare the two models over different set of data from different companies over a period of 750 trading days. In all the cases we find that the Kalman filter algorithm significantly adds value to the forecasting process.


Keywords: Artificial Neural Networks, Kalman filter, Stock prices, Forecasting, Back propagation




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