Multi-model tourist forecasting: case study of Kurdistan Region of Iraq
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DOIhttp://dx.doi.org/10.21511/tt.2(1).2019.04
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Article InfoVolume 2 2019, Issue #1, pp. 24-34
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The tourism industry has been one of the leading service industries in the global economy in recent years and the number of international tourism in 2018 reached 1.4 billion. The goal of the research is to evaluate the performance of various methods for forecasting tourism data and predict the number of tourists during 2019 and 2022. Performance of 15 prediction models (i.e. Local linear structural, Naïve, Holt, Random walk, ARIMA) was compared. Based on error measurements matrix (i.e. RMSE, MAE, MAPE, MASE), the most accurate method was selected to forecast the total number of tourists from 2019 to 2022 to Kurdistan Region (KR), then forecasts were performed for each governorate in KR. The results show that among 15 examined models of tourist forecasting in KR, Local linear structural and ARIMA (7,3,0) model performed best. The number of tourists to KR and each governorate in KR is predicted to increase by most experimented models, especially those which demonstrated higher accuracy. Generally, the number of tourist to KR predicted by ARIMA (7,3,0) is a lot bigger than Local linear structure. Linear structural predicted the number increase to 3,137,618 and 3,462,348 in 2020 and 2022, respectively, while ARIMA (7,3,0) predicted the number of tourists to KR to increase rapidly to 3,748,416 and 8,681,398 in 2020 and 2022.
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JEL Classification (Paper profile tab)L83, C53
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References32
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Tables8
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Figures4
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- Figure 1. Total tourist to KR (2007–2018) along with 4-year forecasts and 80% and 95% prediction intervals
- Figure 2. Total tourist to Erbil Governorate (2007–2018) along with 4-year forecasts and 80% and 95% prediction intervals
- Figure 3. Total tourist to Sulaymaniyah Governorate (2007–2018) along with 4-year forecasts and 80% and 95% prediction intervals
- Figure 4. Total tourist to Duhok Governorate (2007–2018) along with 4-year forecasts and 80% and 95% prediction intervals
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- Table 1. Accuracy assessment of tourist forecasting models
- Table 2. Forecasted number of tourists to KR from Local linear structural
- Table 3. Forecasted number of tourists to KR from ARIMA (7,3,0)
- Table 4. Forecasted number of tourists to Erbil Governorate from Local linear structural model
- Table 5. Forecasted number of tourists to Erbil Governorate from ARIMA (7,3,0)
- Table 6. Forecasted number of tourists to Sulaymaniyah Governorate from Local linear structural model
- Table 7. Forecasted number of tourists to Duhok Governorate from Local linear structural model
- Table 8. Forecasted number of tourists to Duhok Governorate from ARIMA (7,3,1)
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- Altaee, H. H. A., Tofiq, A. M., & Jamel, M. M. (2017). Promoting the Tourism Industry of Kurdistan Region of Iraq (Halabja Province as a Case Study). Journal of Tourism and Hospitality Management, 5(10), 103-111.
- Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International Journal of Forecasting, 16(4), 521-530.
- Athanasopoulos, G., Hyndman, R. J., Song, H., & Wu, D. C. (2011). The tourism forecasting competition. International Journal of Forecasting, 27(3), 822-844.
- Bergmeir, C., Hyndman, R. J., & Benítez, J. M. (2016). Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. International Journal of Forecasting, 32(2), 303-312.
- Bermúdez, J. D., Corberán-Vallet, A., & Vercher, E. (2009). Multivariate exponential smoothing: A Bayesian forecast approach based on simulation. Mathematics and Computers in Simulation, 79(5), 1761-1769.
- Chen, R. J., Bloomfield, P., & Cubbage, F. W. (2008). Comparing forecasting models in tourism. Journal of Hospitality & Tourism Research, 32(1), 3-21.
- Chhorn, T., & Chaiboonsri, C. (2017). Modelling and Forecasting Tourist Arrivals to Cambodia: An Application of ARIMA-GARCH Approach. Journal of Management, Economics, and Industrial Organization, 2(2), 1-19.
- Cho, V. (2001). Tourism forecasting and its relationship with leading economic indicators. Journal of Hospitality & Tourism Research, 25(4), 399-420.
- Claveria, O., Monte, E., & Torra, S. (2016). Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model. SERIEs, 7(3), 341-357.
- Cura, F., Singh, U.-S., & Talaat, K. (2017). Measuring the efficiency of tourism sector and the effect of tourism enablers on different types of tourism (Kurdistan). Turizam, 21(1), 1-18.
- De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513-1527.
- Ekanayake, E. M., & Long, A. E. (2012). Tourism development and economic growth in developing countries. The International Journal of Business and Finance Research, 6(1), 51-63.
- Ellis, P. (2016). Analysis with the 2010 tourism forecasting competition data.
- Goh, C., & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 23(5), 499-510.
- Haslett, J., & Raftery, A. E. (1989). Space-time modelling with long-memory dependence: Assessing Ireland’s wind power resource. Journal of the Royal Statistical Society. Series C (Applied Statistics), 38(1), 1-50.
- Hyndman, R. J., & Khandakar, Y. (2007). Automatic time series for forecasting: the forecast package for R. Journal of Statistical Software, 27(3), 1-22.
- Hyndman, R. J., Akram, M., & Archibald, B. C. (2008). The admissible parameter space for exponential smoothing models. Annals of the Institute of Statistical Mathematics, 60(2), 407-426.
- Hyndman, R. J., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O’Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., & Yasmeen, F. (2018). Forecast: Forecasting functions for time series and linear models.
- Hyndman, R. J., King, M. L., Pitrun, I., & Billah, B. (2005). Local linear forecasts using cubic smoothing splines. Australian and New Zealand Journal of Statistics, 47(1), 87-99.
- Li, G., Song, H., & Witt, S. F. (2005). Recent developments in econometric modeling and forecasting. Journal of Travel Research, 44(1), 82-99.
- Lin, C.-C., Lin, C.-L., & Shyu, J. Z. (2014). Hybrid multi-model forecasting system: A case study on display market. Knowledge-Based Systems, 71, 279-289.
- Loganathan, N., & Yahaya, I. (2010). Forecasting international tourism demand in Malaysia using Box Jenkins sarima application. South Asian Journal of Tourism and Heritage, 3(2), 50-60.
- Medeiros, M. C., McAleer, M., Slottje, D., Ramos, V., & Rey-Maquieira, J. (2008). An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals. Journal of Econometrics, 147(2), 372-383.
- Palmer, A., Montano, J. J., & Sesé, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27(5), 781-790.
- Petrevska, B. (2017). Predicting tourism demand by A.R.I.M.A. models. Economic Research-Ekonomska Istraživanja, 30(1), 939-950.
- Rasaiah, J. (2016). The Future of Tourism in Iraqi Kurdistan: Opportunities and Challenges. Middle East Research Institute, 3(7), 1-4.
- Singh, E. H. (2013). Forecasting Tourist Inflow in Bhutan using Seasonal ARIMA. International Journal of Science and Research, 2(9), 242-245.
- Song, H., & Li, G. (2008). Tourism demand modelling and forecasting – A review of recent research. Tourism Management, 29(2), 203-220.
- Unakıtan, G., & Akdemir, B. (2015). Prediction of Combine’s Number by Using ARMAX Model in Turkey. Balkan and Near Eastern Journal of Social Sciences, 01(01), 1-6.
- UNWTO. (2019). UNWTO World Tourism Barometer and Statistical Annex, January 2019. World Tourism Organization UNWTO.
- USAID. (2008). Kurdistan Region: Economic Development Assessment. United States Agency for International Development.
- Witt, S., & Song, H. (2001). Chapter 9 – Forecasting future tourism flows. In A. Lockwood, & S. Medlik (Eds.), Tourism and Hospitality in the 21st Century (pp. 106-118).