Itsarawadee Hema and Narissara Eiamkanitchart

Published in Data Science and Engineering (DSE) Record 2022 Vol. 3 No. 1 pp. 1-11



The objectives of this independent study consisted of two areas. Firstly, to select appropriate variables of the knowledge-based economy indicators as alternative indicators for predicting Gross Domestic Product (GDP) growth. Secondly, to develop models for forecasting the GDP growth rate using neu-ro-fuzzy technique and compare the model performance. The data used in this work were collected from the World Bank through an Application Pro-gramming Interface, consisting of 5 regions: East Asia & Pacific, Europe & Central Asia, Latin America & Caribbean, Middle East & North Africa, and South Asia. The study investigated and identified the independent varia-bles of the knowledge-based economy that could be used in the GDP growth rate prediction model along with the development of the Adaptive Neuro-fuzzy Inference System (ANFIS) to predict the GDP growth rate. The performance assessment used the prediction results to compare with the Linear Regression (LR) and Artificial Neural Network (ANN) models, us-ing the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results showed that ANFIS provided the highest accuracy in predicting GDP growth rate in 14 of 15 experiments from three types of data: training dataset, testing dataset, and unseen dataset), while the ANN and LR models are less accurate, respectively. The East Asia & Pacific region has the lowest error of all regions; with the average MAE and RMSE of the testing and un-seen datasets at 0.265% and 0.345%, respectively.