Abstract
Since traditional econometrics cannot guarantee that the parametric estimation based on some of time-series variables provides the best solution for economic predictions. Interestingly, combining with mathematics, statistics, and computer science, the big data analysis and machine learning algorithms are becoming more and more computationally highlighted. In this paper, 29 yearly collective factors, which are qualitative information, quantitative trends, and social movement activities, are employed to process in three machine learning algorithms such as k-Nearest Neighbors (kNN), Tree models and random forests (RF), and Support vector machines (SVM). Technically, collective variables using in this paper were observed from the source agents who successfully accumulated data details from trends of the world for easily accessing, for instance, Google Trends or World Bank Database. With advanced artificial calculations, the empirical result is very precise to real situations. The predicting result also clearly shows Thailand economy would be very active (peak) in the upcoming quarters. Consequently, this advanced artificial learning successfully done in this paper would be the new approach to helpfully provide policy recommendations to authorities, especially central banks.
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Chaiboonsri, C., Wannapan, S. (2019). Big Data and Machine Learning for Economic Cycle Prediction: Application of Thailand’s Economy. In: Seki, H., Nguyen, C., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2019. Lecture Notes in Computer Science(), vol 11471. Springer, Cham. https://doi.org/10.1007/978-3-030-14815-7_29
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