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Forecasting of Indian and foreign tourist arrivals to Himachal Pradesh using Decomposition, Box–Jenkins, and Holt–Winters exponential smoothing methods

Author

Listed:
  • Keerti Manisha

    (National Institute of Technology Hamirpur
    National Institute of Technology Tiruchirappalli)

  • Inderpal Singh

    (National Institute of Technology Hamirpur)

Abstract

Himachal Pradesh offers many tourist experiences to Indian and foreign visitors. The mountainous state experiences a high degree of tourism seasonality, interrupting the efficient operation of the tourism infrastructure. Fundamental requirements for tourism planning are accurate projections of tourist demands. However, estimating future demands becomes challenging because of the high degree of seasonality. The compound and annual growth rate methods are used in state-level tourism research to forecast future growth. Since these models cannot manage seasonality and trends in data series, they inaccurately predict future demand. In this context, forecasting models such as the Decomposition, Box–Jenkins, and Holt–Winters exponential smoothing methods were used to forecast the seasonal tourism demands in the study area. The dataset utilized for the analysis was the monthly Indian and foreign tourist arrivals from 2008 to 2018. Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Theil’s U1 coefficients validated that the forecast models produced accurate results. The Box–Jenkins model accurately forecasted the tourist arrivals (2019 to 2031) as reflected by the lowest error metrics (Indian: RMSE 36833.8, MAPE 3.0, Theil's U1 0.023; Foreign: RMSE 6907.59, MAPE 15.51, Theil's U1 0.10). This approach outperformed the traditional seasonal data series forecasting techniques and contributed to the literature on univariate tourist demand forecasting for hilly areas experiencing a high degree of seasonality. Estimating maximum tourist arrivals is crucial in long-term strategic planning for tourism expansion to withstand maximum loads and ensure efficient business flow, higher investment, enhanced economic growth, and environmental protection in the state. This study is a pioneer in examining tourism demands for stakeholders, tourism operators, and planners to plan the fluctuating tourism in Himachal Pradesh while adhering to sustainability principles. Furthermore, it provides inputs for effective planning and policy formulations specific to the tourism industry based on future demand assessments.

Suggested Citation

  • Keerti Manisha & Inderpal Singh, 2024. "Forecasting of Indian and foreign tourist arrivals to Himachal Pradesh using Decomposition, Box–Jenkins, and Holt–Winters exponential smoothing methods," Asia-Pacific Journal of Regional Science, Springer, vol. 8(3), pages 879-909, September.
    • Handle: RePEc:spr:apjors:v:8:y:2024:i:3:d:10.1007_s41685-024-00344-8
    DOI: 10.1007/s41685-024-00344-8
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    References listed on IDEAS

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    1. Anna Serena Vergori, 2012. "Forecasting Tourism Demand: The Role of Seasonality," Tourism Economics, , vol. 18(5), pages 915-930, October.
    2. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844, July.
    3. Apostolos Ampountolas, 2021. "Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models," Forecasting, MDPI, vol. 3(3), pages 1-16, August.
    4. Andrea Kolková & Petr Rozehnal, 2022. "Hybrid demand forecasting models: pre-pandemic and pandemic use studies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(3), pages 699-725, September.
    5. Song, Haiyan & Li, Gang & Witt, Stephen F. & Athanasopoulos, George, 2011. "Forecasting tourist arrivals using time-varying parameter structural time series models," International Journal of Forecasting, Elsevier, vol. 27(3), pages 855-869.
    6. Liu, Ying & Wen, Long & Liu, Han & Song, Haiyan, 2024. "Predicting tourism recovery from COVID-19: A time-varying perspective," Economic Modelling, Elsevier, vol. 135(C).
    7. J. D. Bermudez & J. V. Segura & E. Vercher, 2007. "Holt-Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(9), pages 1075-1090.
    8. Shujie Shen & Gang Li & Haiyan Song, 2009. "Effect of Seasonality Treatment on the Forecasting Performance of Tourism Demand Models," Tourism Economics, , vol. 15(4), pages 693-708, December.
    9. S.C. Thushara & Jen-Je Su & Jayatilleke S. Bandara, 2019. "Forecasting international tourist arrivals in formulating tourism strategies and planning: The case of Sri Lanka," Cogent Economics & Finance, Taylor & Francis Journals, vol. 7(1), pages 1699884-169, January.
    10. Niematallah Elamin & Mototsugu Fukushige, 2018. "Forecasting extreme seasonal tourism demand: the case of Rishiri Island in Japan," Asia-Pacific Journal of Regional Science, Springer, vol. 2(2), pages 279-296, August.
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