Author

Satish Chaudhary and Dipika Uprety

Abstract

Forecasting Nepal’s Gross Domestic Product (GDP) holds paramount importance for effective resource planning and allocation. In this research, Artificial Neural Networks (ANNs) have been introduced to predict the GDP time series, wherein the data have been dissected into linear and nonlinear components. The linear aspects have been handled by the ARIMA model, while the ANNs managed the nonlinear elements. Additionally, the study has delved into hybrid models, resulting in additive and multiplicative combinations of ARIMA and ANN. These hybrid models have aimed to enhance forecasting performance, minimize errors, and improve accuracy compared to standalone models. The findings revealed that both ANN and hybrid models surpassed other approaches in terms of prediction accuracy.