Hybrid BRNN-ARIMA Model for Financial Time Series Forecasting

Abstract

The accurate forecasting of time series is difficult and for exchange rate more difficult as well. Because it is difficult to predict as they continuously fluctuate during trading hours. Exchange rate forecasting plays a vital financial problem in recent era. It is extensively acknowledged that exchange rate stability implies that macroeconomic stability. In this study, a hybrid model is proposed to forecast exchange rates. Bayesian regularized neural network (BRNN) model is assembled with Autoregressive integrated moving average model (ARIMA) and develop hybrid BRNN-ARIMA model. Furthermore, the comparison of proposed hybrid model has been done with standalone BRNN, standalone ARIMA and random walk model. Quarterly exchange rate data from 1970Q1 to 2021Q2 of six countries comprises developed (UK, Canada, and Singapore) and developing (Pakistan, India, and Malaysia) are forecast. To evaluate the performance of these models RMSE, MAE and MAPE are applied. The results indicate that the proposed hybrid BRNN-ARIMA model outperforms the other studied model in forecasting exchange rates.