Multi-model tourist forecasting: case study of Kurdistan Region of Iraq
-
Author(s)Azad RasulLink to ORCID Index: https://orcid.org/0000-0001-5141-0577
,
Amanj Ahmad Hamdamin DewanaLink to ORCID Index: https://orcid.org/0000-0002-3597-3422
,
Saadaldeen Muhammad Nuri SaedLink to ORCID Index: https://orcid.org/0000-0002-1914-8406
-
DOIhttp://dx.doi.org/10.21511/tt.2(1).2019.04
-
Article InfoVolume 2 2019, Issue #1, pp. 24-34
- Cited by
- 1159 Views
-
155 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
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.
- Keywords
-
JEL Classification (Paper profile tab)L83, C53
-
References32
-
Tables8
-
Figures4
-
- 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
-
- 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)
-
- 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).
-
Current trends in global demographic processes
Sergii Sardak ,
Maxim Korneyev ,
Vladimir Dzhyndzhoian ,
Tatyana Fedotova ,
Olha Tryfonova
doi: http://dx.doi.org/10.21511/ppm.16(1).2018.05
Problems and Perspectives in Management Volume 16, 2018 Issue #1 pp. 48-57 Views: 3677 Downloads: 1068 TO CITE АНОТАЦІЯCurrent local and national demographic trends have deepened the existing and formed new global demographic processes that have received a new historical reasoning that requires deep scientific research taking into account the influence of the multifactorial global dimension of the modern society development.
The purpose of the article is to study the development of global demographic processes and to define the causes of their occurrence, manifestations, implications and prospects for implementation in the first half of the 21st century.
The authors have identified and characterized four global demographic processes, namely population growth, migration, increase of tourism, and change in population structure. It is projected that in the 30’s of the 21st century, the number and growth rates of the world population will reach the objective growth and these dynamics over the next two decades will begin to change in the direction of reducing the growth rates, which will lead to gradual stabilization, and eventually reduce the size of the world population. By the middle of the 21st century, one can observe the preservation of the growth rates of international and domestic migration, the growth of international migration flows from the South to the North and from the East to the West, the strengthening of new economically developed centers of gravity (Canada, Australia and New Zealand), the increase in migration of rural population to cities, as well as urbanization and activation of the metropolises development. The share of international tourists in comparison with the world population will be constantly increasing, and the annual growth rate of the number of international tourists will significantly depend on the world economy and may vary at the several percent level. Permanent change will occur in the age, religious-cultural and socio-economic structure of the population. -
Travel and tourism: growth potentials and contribution to the GDP of Saudi Arabia
Anis Ali
doi: http://dx.doi.org/10.21511/ppm.16(1).2018.39
Problems and Perspectives in Management Volume 16, 2018 Issue #1 pp. 417-427 Views: 2368 Downloads: 403 TO CITE АНОТАЦІЯSaudi Arabia is well known in the world for the holy places Makkah and Madinah. Hajjis and visitors come to perform hajj and Umrah every year in Makkah and Madinah from all around the world. Hajj and Umrah visits play a vital role in the inbound tourism of Saudi Arabia. The tourism revenue of Kingdom of Saudi Arabia is increasing absolutely but losing its contribution to the GDP proportionately. So, this paper tries to find out the economic activities of all components of tourism sector which are responsible for the negative trend. It also explains the relationships and variability of tourism revenues and expenditures with GDP and offers suggestions to enhance revenue of tourism sector. This study is based upon secondary data obtained from the website of SAMA (Saudi Arabian Monetary Authority) and General Authority for Statistics of Saudi Arabia. In tourism sector of Saudi Arabia, the increasing trend of expenditures of some economic activities is more than its revenue. The increasing trend of expenditures comparatively its revenues lowering contribution of tourism towards GDP. To improve the level of tourism revenue, government has to provide world class facilities and develop new tourist spots and allow the visitors to visit such places according to the Shariah and Kingdom rules. Government has to focus on developing passenger rail transport and other means of transport to connect all the tourist spots to provide easy conveyance, and on improving and developing tourism sector by helping the government and private organizations that are engaged directly or indirectly in providing services to the domestic and inbound tourists to assure more contribution to GDP.
-
Measuring the inclusiveness of international financing to tourism in Latin America and the Caribbean
Isabel Carrillo-Hidalgo ,
Juan Ignacio Pulido-Fernández
doi: http://dx.doi.org/10.21511/imfi.15(3).2018.02
Investment Management and Financial Innovations Volume 15, 2018 Issue #3 pp. 15-34 Views: 2199 Downloads: 214 TO CITE АНОТАЦІЯGlobally, tourism has been identified as a means of poverty reduction and development, and as a means of encouragement of females, minorities and small businesses to better engage in the mainstream of economic life. This paper examines whether the international and governmental financial support, grated by international financial institutions, is effectively achieving these aims in Latin America and the Caribbean. A series of indices are established in the paper that assess the extent to which such funding includes non-corporate enterprise while also considering the volume and nature of such funding. It is concluded that the goals of inclusiveness are not being met.
