Nowcasting and Forecasting for Thailand’s Macroeconomic Cycles Using Machine Learning Algorithms

Abstract

© 2020, Springer Nature Switzerland AG. With the complexity of social-economic distributional variables, the introduction of artificial intelligent learning approaches was the major consideration of this paper. Machine learning algorithms were fully applied to the multi-analytical time-series processes. Annual macroeconomic variables and behavioral indexes from the search engine database (Google Trends) were observed and they were limited at 2019. The exploration of up-to-date data by the nowcasting calculation based on the Bayesian structural time-series (BSTS) analysis was the solution. To understand Thailand economic cycles, yearly observed GDP was categorized as cyclical movements by the unsupervised learning algorithm called “k-Mean clustering”. To predict three years beforehand, categorized cyclical GDP was estimated with the updated data by using supervised algorithms. Linear Discriminant Analysis (LDA) and k-Nearest Neighbors (kNN) are the two predominant learning predictors contain the highest Kappa’s coefficients and accuracies. The two findings from the two learning approaches are different. The linear-form learning model (LDA) hints expansion periods are still the predictive sign for Thai economy. Conversely, the non-form algorithm (kNN) gives recession signs for Thai economic cycles during the next three years.