Abstract
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.
Highlights
In the literature, one of the most recently explored machine learning (ML) field is the deep learning research area (DL)
The research reported in this paper aims to investigate the potential of the LSTMbased approaches to implement the NARX model
In order to develop a comparative analysis on the use of NARX neural network and long-short time memory (LSTM)
Summary
One of the most recently explored ML field is the deep learning research area (DL). A series of recurrent neural networks, such as convolutional neural networks and long-short time memory (LSTM) have been developed to solve classification and prediction tasks ([13], [14], [15], [16]). The NARX neural network and the LSTM networks are described. For 1 ≤ t ≤ T, Yt is the value at the moment of time t, and we denote by Ŷt the predicted value of Yt. The general NARX forecasting model is expressed by [17]: Ŷ(t+p) = f (Yt(dY), XTt(dXT)) (1). The non-linear function f can be computed by a neural network, the most commonly choice being the NARX networks. The NARX neural networks (NARXNN) are recurrent dynamic networks (RNN) specially tailored to model nonlinear systems, as for instance time series.
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