Euro exchange rate forecasting with differential neural networks with an extended tracking procedure
This paper develops a new kind of non-parametrical artificial neural network useful to forecast exchange rates. We departure from the Differential Neural Networks (DNN) framework and extend the tracking procedure. Under this approach, we examine daily closing exchange rates of Euro against US dollar, Japanese yen and British pound. With our proposal, extended DNN or EDNN, we perform the tracking procedure from February 15, 1999, to August 31, 2013, and, subsequently, the forecasting procedure from September 2 to September 13, 2013. The accuracy of the obtained results is remarkable, since the error percentage in the forecasting period varies from 0.001. © 2016 Academic Publications, Ltd.