Evolving dynamic forecasting model for foreign currency exchange rates using plastic neural networks

This work explores developmental plasticity in neural networks for forecasting the trends in the daily foreign currency exchange rates. With this work we achieved an efficient artificial neural network (ANN) based dynamic prediction model that make use of the trends in the historical daily prices of the foreign currency to predict the future daily rates while modifying its structure with the trends. The plasticity in ANN is explored to achieve a prediction model that is computationally robust and efficient. The system performance analysis prove that the prediction model proposed is efficient, computationally cost effective and unique in terms of its least dependency on the amount of previous data required for the future prediction. The prediction model achieved accuracy as high as 98.852 percent, in predicting a single day's data from ten days data history, over a span of 1000 days (3 years). Further exploration demonstrated that when the problem domain for the network was changed to predict daily currency prices for multiple chunks of days a much better accuracy was achieved. This performance proved the robustness of the model proposed in this work for a modified problem domain.