계절 ARIMA 모형을 이용한 울산 고래관광 수요예측에 관한 연구

Abstract

Accurate and timely tourism demand forecasts are crucial to destination policymaking and planning(Hu, 2022). Demand forecasting is steadily used in the tourism field to respond to environmental changes caused by the dynamics of the tourism market. This study aims to forecast the size of the market considered for seasonality based on past demand data and present a demand prediction model for whale ocean travel ships of whale tourism in Ulsan, Korea. For this, this study collected data from January 2012 to December 2020 from Tourism Knowledge & Information System. In order to achieve the research purpose, a time series analysis of Seasonal ARIMA which is widely used in econometrics was conducted. We intend to compare predictions and observations obtained using the Seasonal ARIMA, that is, the autoregressive moving average model, test the suitability and accuracy of the model, and select the most suitable model which is Seasonal ARIMA(1,1,2)(0,1,1). The result was forecasted that the number of tourism destinations was 182 in first quarter 2021, 297 in second quarter 2021, 497 in third quarter 2021, 493 in fourth quarter 2021, 247 in first quarter in 2022, 314 in second quarter 2022, 503 in third quarter 2022, 496 in fourth quarter 2022, 248 in first quarter in 2023, 316 in second quarter 2023, 505 in third quarter 2023, 497 in fourth quarter 2023. From this point of view, this study is thought to be of great help in accurately creating policies and policies for the development of the tourism economy by predicting the tourism demand of tourism destinations based on theory.