Application of Time-Series Modeling in Forecasting the Doctorate-Level Science and Technology Workforce

Abstract

The science and technology (S&T) workforce plays a crucial role in social development by promoting technological innovation and economic growth, as well as serving as a key indicator of research and development productivity and measure of innovation capability. Therefore, effective S&T workforce policies must be established to enhance national competitiveness. This study proposes a time-series forecasting methodology to predict the scale and structural trends of South Korea’s doctorate-level S&T workforce. Based on earlier research and case data, we applied both the traditional time-series model exponential smoothing and the latest model Prophet, developed by Meta, in this study. Further, public data from South Korea were used to apply the proposed models. To ensure robust model evaluation, we considered multiple metrics. With respect to both forecasting accuracy and sensitivity to data variability, Prophet was found to be the most suitable for predicting the S&T doctorate workforce’s scale. The scenarios derived from the Prophet model can help the government formulate policies based on scientific evidence in the future.