Comparison of Holt Winter's and SARIMA Methods on the data of the Number of Foreign Tourist Visits in Bali Province

Abstract

The tourism sector is a critical component of a country’s economy, including in Indonesia, where its impact is felt both nationally and regionally, such as in provinces and cities. This research focuses on Bali Province and aims to conduct a comparative analysis of the Holt-Winter’s and Seasonal Auto-regressive Integrated Moving Average (SARIMA) methods for forecasting foreign tourist arrivals. The analysis centers on two primary entry points: Ngurah-Rai Airport and the seaport. The primary objective is to forecast the number of foreign tourist arrivals from February 2024 to January 2025. The results indicate that the Holt-Winter’s model has a Mean Absolute Percentage Error (MAPE) of 5.2631%, which is lower than the MAPE of 5.8920% for the SARIMA model. Additionally, the Mean Absolute Error (MAE) for the Holt-Winter’s model is 19,149.18, compared to 20,883.20 for the SARIMA model. Consequently, this study concludes that the Holt-Winter’s model provides more accurate predictions and is closer to the actual values than the SARIMA model. Bali, Holt-Winter’s, forecasting, SARIMA, tourism.