Using Discrete Hidden Markov Model for Modelling and Forecasting the Tourism Demand in Isfahan
محورهای موضوعی : Data MiningKhatereh Ghasvarian Jahromi 1 , Vida Ghasvarian Jahromi 2
1 - ACECR Institute of Higher Education (Isfahan Branch)
2 - University of Science and Arts of Yazd
کلید واژه: Modeling , Tourism Demand Function , Demand Prediction , Discrete Hidden Markov Model , Iran , Isfahan,
چکیده مقاله :
Tourism has been increasingly gaining acceptance as a driving force to enhance the economic growth because it brings the per capita income, employment and foreign currency earnings. Since tourism affects other industries, in many countries, tourism is considered in the economic outlook. The perishable nature of most sections dependent on the tourism has turned the prediction of tourism demand an important issue for future success. The present study, for the first time, uses the Discrete Hidden Markov Model (DHMM) to predict the tourism demand. DHMM is the discrete form of the well-known HMM approach with the capability of parametric modeling the random processes. MATLAB Software is applied to simulate and implement the proposed method. The statistic reports of Iranian and foreign tourists visiting Isfahan gained by Iran Cultural Heritage, Handicrafts, and Tourism Organization (ICHHTO)-Isfahan Tourism used for simulation of the model. To evaluate the proposed method, the prediction results are compared to the results from Artificial Neural Network, Grey model and Persistence method on the same data. Three errors indexes, MAPE (%), RMSE, and MAE, are also applied to have a better comparison between them. The results reveal that compared to three other methods, DHMM performs better in predicting tourism demand for the next year, both for Iranian and foreign tourists.
Tourism has been increasingly gaining acceptance as a driving force to enhance the economic growth because it brings the per capita income, employment and foreign currency earnings. Since tourism affects other industries, in many countries, tourism is considered in the economic outlook. The perishable nature of most sections dependent on the tourism has turned the prediction of tourism demand an important issue for future success. The present study, for the first time, uses the Discrete Hidden Markov Model (DHMM) to predict the tourism demand. DHMM is the discrete form of the well-known HMM approach with the capability of parametric modeling the random processes. MATLAB Software is applied to simulate and implement the proposed method. The statistic reports of Iranian and foreign tourists visiting Isfahan gained by Iran Cultural Heritage, Handicrafts, and Tourism Organization (ICHHTO)-Isfahan Tourism used for simulation of the model. To evaluate the proposed method, the prediction results are compared to the results from Artificial Neural Network, Grey model and Persistence method on the same data. Three errors indexes, MAPE (%), RMSE, and MAE, are also applied to have a better comparison between them. The results reveal that compared to three other methods, DHMM performs better in predicting tourism demand for the next year, both for Iranian and foreign tourists.
[1] F. L. Chu, “Using a logistic growth regression model to forecast the demand for tourism in Las Vegas,” Tour. Manag. Perspect., vol. 12, pp. 62–67, 2014.
[2] “Strategic Plan for the Development of Tourism in Mozambique (2004-2013),” 2013.
[3] U. Gunter and I. Önder, “Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data,” Tour. Manag., vol. 46, pp. 123–135, 2015.
[4] P. F. Pai, K. C. Hung, and K. P. Lin, “Tourism demand forecasting using novel hybrid system,” Expert Syst. Appl., vol. 41, no. 8, pp. 3691–3702, 2014.
[5] B. Peng, H. Song, and G. I. Crouch, “A meta-analysis of international tourism demand forecasting and implications for practice,” Tour. Manag., vol. 45, pp. 181–193, 2014.
[6] H. Song and G. Li, “Tourism demand modelling and forecasting-A review of recent research,” Tour. Manag., vol. 29, no. 2, pp. 203–220, 2008.
[7] M. Akin, “A novel approach to model selection in tourism demand modeling,” Tour. Manag., vol. 48, pp. 64–72, 2015.
[8] F. L. Chu, “Analyzing and forecasting tourism demand with ARAR algorithm,” Tour. Manag., vol. 29, no. 6, pp. 1185–1196, 2008.
[9] A. Guizzardi and A. Stacchini, “Real-time forecasting regional tourism with business sentiment surveys,” Tour. Manag., vol. 47, pp. 213–223, 2015.
[10] H. Song, G. Li, S. Witt, and B. Fei, “Tourism demand modelling and forecasting: how should demand be measured?,” Tour. Econ., vol. 16, no. 1, pp. 63–81, 2010.
[11] A. Palmer, J. José Montaño, and A. Sesé, “Designing an artificial neural network for forecasting tourism time series,” Tour. Manag., vol. 27, no. 5, pp. 781–790, 2006.
[12] E. Hadavandi, A. Ghanbari, K. Shahanaghi, and S. Abbasian-Naghneh, “Tourist arrival forecasting by evolutionary fuzzy systems,” Tour. Manag., vol. 32, no. 5, pp. 1196–1203, 2011.
[13] X. Sun, W. Sun, J. Wang, Y. Zhang, and Y. Gao, “Using a Grey-Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China,” Tour. Manag., vol. 52, pp. 369–379, 2016.
[14] H. Hassani, A. Webster, E. S. Silva, and S. Heravi, “Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis,” Tour. Manag., vol. 46, pp. 322–335, 2015.
[15] C. H. Wang, “Predicting tourism demand using fuzzy time series and hybrid grey theory,” Tour. Manag., vol. 25, no. 3, pp. 367–374, 2004. [16] S. Shen, G. Li, and H. Song, “Combination forecasts of International tourism demand,” Ann. Tour. Res., vol. 38, no. 1, pp. 72–89, 2011. [17] K. Y. Chen, “Combining linear and nonlinear model in forecasting tourism demand,” Expert Syst. Appl., vol. 38, no. 8, pp. 10368–10376, 2011.
[18] D. Cheng and L. Bin Liu, “Forecasting of tourism demand for Guilin based on combined model,” in 7th International Joint Conference on Computational Sciences and Optimization, CSO 2014, 2014, no. 1, pp. 100–103.
[19] X. Wang, H. Zhang, and X. Guo, “Demand Forecasting Models of Tourism Based on ELM,” in 7th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2015, 2015, pp. 326–330.
[20] Y. H. Liang, “Using the combined model for forecasting the tourism demand,” in International Conference on Machine Learning and Cybernetics, 2016, vol. 2, pp. 612–615.
[21] S. Sadatiseyedmahalleh, N. H. Bateni, and N. H. Bateni, “Prediction of Tourism Demand in Iran by Using Artificial Neural Network (ANN) and Supporting Vector Machine (SVR),” Int. J. Multicult. Multireligious Underst., vol. 3, no. 1, pp. 37–44, 2016.
[22] J. Rosselló and A. Sansó, “Yearly, monthly and weekly seasonality of tourism demand: A decomposition analysis,” Tour. Manag., vol. 60, pp. 379–389, 2017.
[23] A. N. Kazak, “Analysis of Dynamics of Demand, Revenue and Ergonomic Aspects of Tourism,” in Third International Conference on Human Factors in Complex Technical Systems and Environments (ERGO)s and Environments (ERGO), 2018, pp. 13–15.
[24] Y. Li and H. Cao, “Prediction for Tourism Flow based on LSTM Neural Network,” Procedia Comput. Sci., vol. 129, pp. 277–283, 2018.
[25] Y. Yao et al., “A paired neural network model for tourist arrival forecasting,” Expert Syst. Appl., vol. 114, pp. 588–614, 2018.
[26] S. Sun, Y. Wei, K. L. Tsui, and S. Wang, “Forecasting tourist arrivals with machine learning and internet search index,” Tour. Manag., vol. 70, pp. 1–10, 2019.
[27] J. Kang, C. Feng, Q. Shao, and H. Hu, “Prediction of Chatter in Machining Process Based on Hybrid SOM-DHMM Architecture,” in ICIC 2007, LNAI 4682, 2007, pp. 1004–1013.
[28] L. R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” in Proceedings of the IEEE, 1989, vol. 77, no. 2.
[29] A. Asrari, D. Seyed Javan, M. Hossein Javidi, and M. Monfared, “Application of Gray-Fuzzy-Markov Chain Method for Day-Ahead Electric Load Forecasting,” Przegląd Elektrotechniczny Electr. Rev., no. 3, pp. 228–237, 2012.
[30] N. Amjady, F. Keynia, and H. Zareipour, “Wind Power Prediction by a New Forecast Engine Composed of Modi fi ed Hybrid Neural Network and Enhanced Particle Swarm Optimization,” IEEE Trans. Sustain. Energy, vol. 2, no. 3, pp. 265–276, 2011.
