Neural Network Model in Forecasting Malaysia’s Unemployment Rates

Abstract

Neural networks (NN) have been widely applied in time series forecasting. This study aims to develop basic NN models for forecasting the unemployment rate in Malaysia by gender. The yearly unemployment rate of thirty-eight years from the year 1982 to 2019 was obtained from the Department of Statistics Malaysia. In addition, datasets of gross domestic product, inflation and population rates extracted from the World Bank Data website were used as input variables in developing the NN models. Several NN models with different number of hidden nodes were developed and evaluated. Results showed that the best model for the male population was the NN model with four hidden nodes in one hidden layer whereas the NN model with two hidden nodes in one hidden layer was the best for the female population. Additionally, it can be concluded that the trend for the future unemployment rate in Malaysia for male and female population in the next ten years will be gradually constant throughout the year starting from 2020 to 2030.

Keywords

• forecasting
• gender
• unemployment rate
• neural network