Strategic Forecasting of Monthly Patent Application Filings: Analyzing Seasonality for Sustainable R&D Governance

Intellectual property (IP) is a cornerstone of sustainable industrial growth, yet unpredictable fluctuations in patent application filings pose a challenge to the administrative efficiency and sustainable governance of patent offices. This study aims to enhance strategic R&D governance by analyzing the seasonality of patent application filings using monthly data from the Republic of Korea (January 2001 to July 2024) and proposing a time series forecasting model that reflects this seasonality. To verify seasonal patterns, visual analyses (graphs, time series decomposition, and autocorrelation function plots) and the Kruskal–Wallis test were conducted. The results confirmed a clear 12-month seasonal pattern, characterized by a distinct “December Rush” at the end of each year. Based on these findings, we compared the autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) models, demonstrating that the SARIMA model offers superior predictive performance by effectively capturing these cyclical fluctuations. Furthermore, by segmenting data into private and public R&D sectors, we observed that private R&D exhibits more pronounced seasonal volatility, necessitating differentiated management strategies. This study highlights the critical role of seasonality in forecasting patent volumes and provides a data-driven framework for sustainable governance, offering actionable insights for optimizing resource allocation and policy support in the innovation ecosystem.