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پیشبینی تقاضای گردشگری خارجی (یک مطالعه موردی برای ایران) | ||
| برنامه ریزی و توسعه گردشگری | ||
| مقاله 3، دوره 4، شماره 13، شهریور 1394، صفحه 51-71 اصل مقاله (605.95 K) | ||
| نوع مقاله: علمی-پژوهشی | ||
| نویسندگان | ||
| حمید ابریشمی1؛ احمد قلی برکیش* 2 | ||
| 1دانشگاه تهران | ||
| 2دانشگاه فردوسی مشهد | ||
| تاریخ دریافت: 03 آبان 1393، تاریخ بازنگری: 21 خرداد 1394، تاریخ پذیرش: 25 شهریور 1394 | ||
| چکیده | ||
| پیشبینی جریان آینده‌ی گردشگری ورودی برای تعیین مخارج سرمایه‌گذاری در صنعت گردشگری، هم برای بخش دولتی و هم برای بخش خصوصی، ضروری است. برای بخش دولتی و عمومی تخمین تقاضای گردشگری به‌منظور استفاده‌ی کارا از صنعت حمل‌ونقل و برنامه‌ریزی در نحوه‌ی تخصیص منابع حیاتی است. همچنین پیشبینی صحیح میتواند برای بخش خصوصی مانند شرکت‌های حمل‌ونقل هوایی در برنامه‌ریزی و طرح‌ریزی خطوط هوایی، تجهیزات، امکانات رفاهی و برنامه‌ریزی برای منابع انسانی مفید باشد. علی‌رغم اهمیت این موضوع در حوزه‌ی گردشگری، مطالعات انجام‌شده‌ی کشور ما در این حوزه بسیار محدود است. از سوی دیگر، ازآنجایی‌که اثبات شده است مدل تک‌متغیره روش بسیار موفقیتآمیزی برای پیشبینی سری زمانی گردشگری است، در این مطالعه با استفاده از داده‌های ماهانه‌ی گمرک جمهوری اسلامی ایران در فاصله‌ی فروردین 1378 تا اسفند 1390، مدل‌های تک‌متغیره‌ی ARFIMA، روش هوشمند ANN و مدل ARFIMA-ANN را که آلاداگو همکاران در سال 2012 پیشنهاد کرده‌اند، برای سری زمانی گردشگری کشور برآورد کردیم و نتایج حاصل از پیشبینی آن‌ها را با یکدیگر مقایسه نموده‌ایم. استفاده از معیارهای RMSE,MAPE,MAE برای ارزیابی صحت پیشبینی افقهای زمانی متفاوت در میان مدل‌های مذکور نشان می‌دهد که مدل ARFIMA-ANN در افقهای زمانی 6،12، 18 و 24 ماه پیش رو توان بالاتری در پیشبینی نسبت به مدل‌های رقیب دارد و میتواند به‌عنوان مدلی مناسب برای برآورد و پیشبینی سری زمانی گردشگری کشور مورد استفاده قرار گیرد. | ||
| کلیدواژه‌ها | ||
| پیشبینی گردشگری؛ مدل ترکیبی؛ مدل ARFIMA؛ ARFIMA-ANN | ||
| عنوان مقاله [English] | ||
| Forecasting International Tourist (A Case Study of Iran) | ||
| نویسندگان [English] | ||
| Hamid Abrishami1؛ Ahmad Gholi barkish2 | ||
| چکیده [English] | ||
| Extended Abstract Forecasting is an essential analytical tool for tourism policy and planning. Business success, marketing decisions, government’s investment policy as well as macroeconomic policy are influenced by the accuracy of tourism forecasts, since the tourism product comprises a number of services that cannot be accumulated, Accurate forecasts of tourism demand are paramount to ensure the availability of such services when demanded. on the other hand since univariate time series modeling has proved to be a very successful method for forecasting tourist arrivals, In this article, along with study of ARFIMA and artificial intelligence methods ,we use the new hybrid approach combining ARFIMA and feed forward neural networks (FNN) proposed by Aladagh et al. in 2012.For this purpose, ARFIMA, ANN and ARFIMA-ANN models are considered and compared their results in different time horizon using IRICA(The Islamic Republic of Iran Customs Administration) monthly time series of tourist arrivals to Iran from march 1999 to march 2012. According to MAPE, RMSE and MAE criteria, the forecasting performance of the ARFIMA-ANN model is better than the ARFIMA and ANN models at the horizons of 6, 12, 18 and 24 months and thus it can be used as suitable model for forecasting tourist arrivals to Iran. Introduction Forecasting is an essential analytical tool for tourism policy and planning. Accurate forecasting of future tourist flow is essential to determine successful investments in the tourism industry for both the public and the private sectors. For the public sector, estimation of tourism demand is important in order to make efficient use of transportation and resources. For the private sector, such as airlines, good tourism forecasting is useful for planning aircraft, facilities and manpower needs (Chang and Liao, 2010:215), despite of the importance of this issue, very little research has been done in this area in IRAN. Therefore In this article, ARFIMA, ANN and ARFIMA-ANN models were used for forecasting of country’s tourism time series. Materials and Methods Since univariate time series modeling has proved to be a very successful method for forecasting tourist arrivals, it is also the method employed in this paper. In order to choose best model for forecasting tourist arrivals to Iran ARFIMA, ANN and ARFIMA-ANN methods were compared to forecast tourist flows to Iran at the horizons of 6, 12, 18 and 24 months using IRICA(The Islamic Republic of Iran Customs Administration) monthly time series of tourist arrivals to Iran from march 1999 to march 2012. Discussion and Results According to MAPE, RMSE and MAE criteria, the best results is obtained by hybrid(ARFIMA-ANN) method which means that ARFIMA-ANN thus it can be used as suitable model for forecasting tourist arrivals to Iran. Conclusions The ARFIMA-ANN method is appropriate method to forecast foreign tourist to Iran and it will can be used as suitable model for forecasting tourist arrivals to Iran. References: Abrishami, H. and Mehrara, M. (2002). Applied econometrics (New approach), Tehran: University of Tehran Press. (In Persian) Aladag, C.H., Egrioglu, E. and Kadilar, C. (2012). Improvement in forecasting accuracy using the hybrid model of ARFIMA and feed forward neural network, American Journal of Intelligent Systems, 2(2):12-17. Archer, B.H. (1987). Demand forecasting and estimation, In Ritchie, J.R.B. and Goeldner, C.R. (Eds) Travel, tourism and hospitality research, New York: Wiley. Aslanargun, A., Mammadov, M., Yazici, B. and Yolacan, S. (2007). Comparison of ARIMA, neural networks and hybrid models in time series: Tourist arrival forecasting, Journal of Statistical Computation and Simulation, 77: 29–53. Athanasopoulos, G., Hyndman, R.J., Song, H. and Wu, D.C. (2011). The tourism forecasting competition, International Journal ofForecasting, 27: 822–844. Burger, C.J.S.C., Dohnal, M., Kathrada, M. and Law, R. (2001). A practitioners guide to time-series methods for tourism demand forecasting–A case study of Durban, South Africa, Tourism Management, 22: 403–409. Claveria, O., Monte, E. and Torra, S. (2014). Tourism demand forecasting with neural networkmodels: Different ways of treating information, International Journal of Tourism Research, 1-20, DOI: 10.1002/jtr.2016. Claveria, O. and Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. time series models, Economic Modelling, 36: 220-228. Chang, Y.W. and Liao, M.Y. (2010). A seasonal ARIMA model of tourism forecasting: The case of Taiwan, Asia Pacific journal of Tourism research, 15(2): 215-221. 10. Chu, F.L. (2008). A fractionally integrated autoregressive moving average approach to forecasting tourism demand, Tourism Management, 29: 79-88. 11. Chu, F.L. (2009). 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Combining linear and nonlinear model in forecasting tourism demand, Expert Systems with Applications, 38: 10368–10376. 18. Lewis, C.D. (1982). International and business forecasting methods, London: Butterworths. 19. Lildhold, P. (2000). Long memory an ARFIMA modeling, University of Aarhus. 20. Loganathan, N. and Ibrahim, Y. (2010). Forecasting international tourism demand in Malaysia using Box Jenkins sarima application, South Asian Journal of Tourism and Heritage, 3(2): 50-59. 21. Saayman, A. and Saayman, M. (2010). Forecasting tourist arrivals in South Africa, Acta Commercii, 10: 281-293. 22. Shitan, M. (2008). Time series modelling of tourist arrivals to Malaysia, InterStat, (October): 1-12. 23. Song, H. and Li, G. (2008). Tourism demand modelling and forecasting–A review of recent research, Tourism Management, 29 (2): 203-220. 24. Song, H. and Witt, S.F. (2006). Forecasting international tourist flows to Macau, Tourism Management, 27: 214-224. 25. Teixeira, J. P. and Fernandes, P.O. (2012). Tourism time series forecast-different ANN architectures with time index input, Procedia Technology, 5: 445-454. 26. Tseng, F.M., Yu, H.C. and Tzeng, G.H. (2002). Combining neural network with seasonal time series ARIMA model, Technological Forecasting and Social Change, 69(1):71–87. 27. UNWTO. (2012). ‘Why tourism?’ [Online article]. Available from: http: // www2.unwto.org/en/ content/why- tourism, [Accessed 15 September 2012]. 28. Xiu, J. and Jin, Y. (2007). Empirical study of ARFIMA model based on fractional differencing, Physica A, 377:138 –154. 29. Zhang, G.P. (2003). Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, 50: 159-175. 30. Zhang, G.P., Patuwo, E.B. and Hu, M.Y. (1998). Forecasting with artificial neural networks: the state of the art, International Journal of Forecasting, 14: 35–62. 31. Zhang, G.P., Patuwo, E.B. and Hu M.Y. (2001). A simulation study of artificial neural networks for nonlineartime-series forecasting, Computer & Operation Research, 28: 381–396. | ||
| کلیدواژه‌ها [English] | ||
| ARFIMA, ARFIMA-ANN, hybrid method, tourism forecasting | ||
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