Forecasting Financial Time Series using a Hybrid Non-stationary Model with ANN - Shetty, D.K; Ismail, B (International Journal of Computer Sciences and Engineering, 2019)
Forecasting financial time series have been regarded as one of the most challenging applications of modern time series forecasting. Thus, numerous models have been depicted to provide the investors with more precise predictions. In recent years, financial market dynamics forecasting has been a focus of economic research. In this paper, we propose a hybrid nonstationary time series model with artificial neural network (ANN) for forecasting financial time series. The proposed model is non-stationary in trend component with regressor, lagged variable and non-linear component. The proposed model can capture both linear and non-linear structures in the time series. Non-linear structure is capture by Fed-Forward Neural Networks (FNN). The working of the proposed model is examined for SPY and VOO stock prices. Forecast based on the proposed model performs better than existing models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percent Error (MAPE) criterion.