Abstract
The cafe business in Busan exhibits an annual growth trend, driven by an improving economic level and an increasing demand. Trends and consumption patterns in the cafe industry are subject to various influencing factors, and the volatility has intensified, particularly due to unexpected societal changes such as COVID-19. This study aims to analyze and predict trends in Busan's cafe business by considering population trends, the number of COVID cases, and the Consumer Price Index (CPI) of Busan. In particular, according to customer usage patterns, we will classify cafes as in-store cafe and takeout cafe, and analyze trends and forecasts of the number of cafes in a multivariate time series. The data used in this paper is collected and utilized from public data, and the open source Python programming language is used for analysis. The Busan cafe commercial district analysis method uses regression analysis, SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous), and VAR (Vector Autoregression). Through this, we understand how the two types of cafes are affected by environmental variables and compare the performance and prediction ability of each analysis method. The results derived from this are expected to provide useful insights into the future development direction and prediction of the local cafe commercial area.
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