The Use of LSTM Neural Networks to Implement the NARX Model. A Case Study of EUR-USD Exchange Rates

The paper focuses on financial data forecasting in terms of one-step-ahead nonlinear model with exogenous inputs. The main aim is the development of a methodology to forecast the exchange rate between EURO and US Dollar. The prediction task is carried out by two recurrent neural networks, the standard NARX neural network and a LSTM-based approach. The exogenous inputs consist of historical trading data and three widely used technical indicators, namely a variant of moving average, the Upper Bollinger Frequency Band and the Lower Bollinger Frequency Band. In order to obtain accurate forecasting algorithms, the exogenous inputs are filtered using the well-known Gaussian low-pass filter. The quality of each method is evaluated in terms of both quantitative and qualitative metrics, namely the root mean squared error, the mean absolute percentage error, and the prediction of change in direction. Extensive experiments point out that the most suited forecasting method is based on the proposed LSTM neural network for NARX model.