The paper advances an original artificial intelligence-based mechanism for specific economic predictions. The aim is to forecast the exchange rate of euro versus the Romanian currency using a large set of financial data. The possible influence of specific forecasting indicators (such as Sibiu Futures Stock Exchange market) on the evolution of the exchange rate in Romania is also analyzed. The time series under discussion are inherently non-stationary. This aspect implies that the distribution of the time series changes over time. The recent data points could provide more important information than the far distant data points. Therefore, we propose a new adaptive retraining mechanism to take this characteristic into account. The algorithm establishes how a viable structure of an artificial neural network (ANN) at a previous moment of time could be retrained in an efficient manner, in order to support modifications in a complex input-output function of a financial forecasting system. In this system, all the inputs and outputs vary dynamically, and different time delays might occur. A “remembering process” for the former knowledge achieved in the previous learning phase is used to enhance the accuracy of the predictions. The results show that the first training (which includes the searching phase for the optimal architecture) always takes a relatively long time, but then the system can be very easily retrained, since there are no changes in the structure. The advantage of the retraining procedure is that some relevant aspects are preserved (“remembered”) not only from the immediate previous training phase, but also from the previous but one phase, and so on. A kind of “slow forgetting process” also occurs; thus for the ANN it is much easier to remember specific aspects of the previous training instead of the first training. The experiments reveal the high importance of the retraining phase as an upgrading/updating process and the effect of ignoring it, as well. There has been a decrease in the test error when successive retraining phases were performed and the neural system accumulated experience.
