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Abstract
This study presents a comprehensive time series analysis of international tourist arrivals to Riau Province, Indonesia, from 2019 to 2024. The research aims to model, compare, and forecast monthly tourist arrivals using the Holt-Winters additive and multiplicative exponential smoothing methods, implemented in R. The dataset, sourced from the official BPS-Statistics Indonesia Riau Province and covering the pre-pandemic, pandemic, and recovery periods, provides a unique opportunity to assess the impact of global disruptions and subsequent recovery on regional tourism. Data preprocessing, model fitting, and evaluation were conducted in R, with model performance assessed using Mean Absolute Error (MAE). The results indicate that the Holt-Winters multiplicative model outperforms the additive model, particularly in capturing the pronounced seasonal fluctuations and level-dependent variance observed in the data. Forecasts for 2025 suggest a continued recovery in international arrivals, with implications for tourism planning and policy in Riau. The study underscores the value of robust time series forecasting for evidence-based decision-making in regional tourism management and highlights the adaptability of R for reproducible, transparent analysis. Recommendations are provided for future research, including the integration of exogenous variables and the exploration of advanced forecasting techniques.
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References
investigation,” Journal of Travel Research, vol. 51, no. 4, pp. 388–399, 2012.
2. A. Goh and R. Law, “Tourism demand forecasting for the Greater China region: A time series approach,” Asia Pacific Journal of Tourism Research, vol. 8, no. 1, pp. 75–88, 2003.
3. A. Makridakis, S. Wheelwright, and R. Hyndman, Forecasting: Methods and Applications, 3rd ed.,
New York: Wiley, 1998.
4. A. S. Putra and R. Hidayat, “Forecasting international tourist arrivals in Bali using Holt-Winters exponential smoothing,” Jurnal Pariwisata Indonesia, vol. 14, no. 2, pp. 45–56, 2021.
5. Badan Pusat Statistik Provinsi Riau, Statistik Wisatawan Mancanegara Riau 2019–2024, Pekanbaru: BPS Riau, 2025.
6. C. Chatfield, The Analysis of Time Series: An Introduction, 6th ed., Boca Raton: CRC Press, 2003.
7. G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control, 5th ed., Hoboken: Wiley, 2015.
8. G. Song and G. Li, “Tourism demand modelling and forecasting—A review of recent research,” Tourism Management, vol. 29, no. 2, pp. 203–220, Apr. 2008.
9. Kementerian Pariwisata dan Ekonomi Kreatif Republik Indonesia, Laporan Kinerja Pariwisata Indonesia 2020–2024, Jakarta: Kemenparekraf, 2024.
10. M. Goh and R. Law, “Modeling and forecasting tourism demand for arrivals with stochastic models,” Tourism Management, vol. 23, no. 5, pp. 499–510, Oct. 2002.
11. M. R. Hasan, “Tourism forecasting using time series models: Evidence from Malaysia,” International Journal of Economics and Management, vol. 11, no. 2, pp. 321–335, 2017.
12. N. Sari, “Analisis peramalan kunjungan wisatawan mancanegara ke Indonesia dengan metode Holt-Winters,” Jurnal Statistika dan Komputasi, vol. 9, no. 1, pp. 33–42, 2022.
13. OECD, Tourism Trends and Policies 2024, Paris: OECD Publishing, 2024.
14. P. R. Winters, “Forecasting sales by exponentially weighted moving averages,” Management Science, vol. 6, no. 3, pp. 324–342, Apr. 1960.
15. R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd ed., Melbourne: OTexts, 2021.
16. S. S. Makridakis et al., “The accuracy of extrapolation (time series) methods: Results of a forecasting competition,” Journal of Forecasting, vol. 1, no. 2, pp. 111–153, 1982.
17. S. Witt and C. Witt, Modeling and Forecasting Demand in Tourism, London: Academic Press, 1992.
18. World Tourism Organization (UNWTO),
International Tourism Highlights 2023 Edition, Madrid: UNWTO, 2023.
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