Forecasting CPI of restaurants and hotels in Korea using the seasonal autoregressive integrated moving average (SARIMA) model

Abstract

Economic development is influenced by many factors, one of which is the inflation rate. The consumer price index (CPI) is a commonly used indicator for measuring the inflation rate. In recent years, CPI prediction has attracted the attention of many scholars due to its excellent measure of macroeconomic performance. It is very essential to develop an accurate and precise forecasting model. This research fitted and forecasted the CPI of restaurants and hotels in South Korea using the seasonal autoregressive integrated moving average (SARIMA) model. In contrast to other time series models, the SARIMA model incorporates the seasonal component of a time series to improve the accuracy of its forecasts. The time series data were obtained from the website of Statistics Korea (KOSTAT) for the monthly CPI of restaurants and hotels in South Korea from January 2010 to December 2022. Data were analyzed using R-Statistics software and EViews. In this paper, the best model was based on the results of mean absolute percentage error (MAPE) and R2. A detailed description of model selection and predictive accuracy is provided. The findings suggest that the proposed research approach achieves good prediction accuracy from a range of different SARIMA models. Therefore, the developed SARIMA model can be considered for forecasting of monthly CPI of restaurants and hotels in South Korea.