Time Series Analysis of Tourist Arrivals to Bali Using Data - Kusuma, Aniek Suryanti; Batubulan, Kadek Suarjuna (INFOTEKS (Information Technology, Computer and Sciences), 2025)
This research performs a time series analysis on the number of tourist arrivals to Bali, using historical data to identify patterns, trends, and potential forecasting models. The tourism sector is crucial to Bali's economy, and understanding visitor trends can assist in planning and resource allocation. Data from 2010 to 2023 is analyzed, focusing on monthly arrival statistics sourced from government tourism departments. Several time series methods are employed, including seasonal decomposition, autocorrelation, and ARIMA (AutoRegressive Integrated Moving Average) modeling. The analysis reveals distinct seasonal patterns, with peaks during holiday periods and off-peak lulls. A significant impact of global events, such as the COVID-19 pandemic, is observed, causing sharp declines in tourist arrivals. By fitting ARIMA models, we forecast future trends in tourist numbers, providing insights into the potential recovery trajectory of Bali's tourism industry post-pandemic. The research concludes with recommendations for stakeholders, including government agencies and businesses, on how to prepare for future fluctuations in tourist arrivals and capitalize on seasonal trends. Understanding these patterns is essential for fostering sustainable growth and minimizing economic disruptions within the tourism sector.