Using the wide and deep flexible neural tree to forecast the exchange rate

Abstract

Forecasting exchange rate plays an important role in the financial market. It has become a hot research topic and many methods have been proposed. In this paper, a wide and deep flexible neural tree (FNT) is proposed to forecast the exchange rate. The wide component has the function to memorize the original input features, while the deep component can automatically extract unseen features. By balancing the width and depth of flexible neural tree, the structure of FNT is optimized from the experiments to forecast the exchange rate. Experiments have been conducted on four different kinds of exchange rate daily data to check the performance of the FNT. The architecture of the wide and deep FNT is developed by grammar guided genetic programming (GGGP) and the parameters are optimized by the particle swarm optimization algorithm (PSO). Proposed method performs well as compared to the autoregressive moving average model and neural networks.