Developing tourism demand forecasting models using machine learning techniques with trend, seasonal, and cyclic components

Abstract

—This paper proposes the deterministic generation of auxiliary variables, which outline the seasonal, cyclic and trend components of the time series associated with tourism demand for the machine learning models. To test the contribution of the deterministically generated auxiliary variables, we have employed multilayer perceptron (MLP) regression, and support vector regression (SVR) models, which are the well-known stateof- art machine learning models. These models are used to make multivariate tourism forecasting for Turkey respected to two data sets: raw data set and data set with deterministically generated auxiliary variables. The forecasting performances are compared regards to these two data sets. In terms of relative absolute error (RAE) and root relative squared error (RRSE) measurements, the proposed machine learning models have achieved significantly better forecasting accuracy when the auxiliary variables have been employed

Keywords

• Developing
• tourism
• demand

References

1. Haiyan Songa and Gang Li, "Tourism demand modelling and forecasting - A review of recent research," Tourism Management, no. 29, pp. 203–220, 2008.
2. Rob J Hyndman, "Forecasting overview," 2009.
3. R Adhikari and RK Agrawal, "Forecasting strong seasonal time series with artificial neural networks," Journal of Scientific & Industrial Research, vol. 71, pp. 657-666, 2012.
4. Tim Hill, Marcus O'Connor, and William Remus, "Neural Network Models for Time Series Forecasts," Management Science, vol. 42, no. 7, pp. 1082-1092, 1996.
5. J.H. Wang and J.Y. Leu, "Stock market trend prediction using ARIMA- based neural networks," in IEEE Int. Conf. Neural Networks , 1996, pp. 2160–2165.
6. Fang-Mei Tseng, Hsiao-Cheng Yu, and Gwo-Hsiung Tzeng, "Combining neural network model with seasonal time series ARIMA model," Technological Forecasting and Social Change, vol. 69, no. 1, pp. 71-87, 2002.
7. Nesreen K. Ahmed, Amir F. Atiya, Neamat El Gayar, and Hisham El- Shishiny, "Tourism Demand Foreacsting Using Machine Learning Methods," International Journal on Artificial Intelligence and Machine Learning, pp. 1-7, 2008.
8. R. Law and N. Au, "A Neural Network Model to Forecast Japanese Demand for Travel to Hong Kong," Tourism Management, vol. 20, pp. 89-97, 1999.

9. R. Law, "Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting," Tourism Management, vol. 21, no. 4, pp. 331-340, 2000.
10. D. Pattie and J. Snyder, "Using a Neural Network to Forecast Visitor Behavior. Annals of Tourism Research," 1996.
11. P. F. Pai and W. C. Hong, "An improved neural network model in forecasting tourist arrivals," Annals of Tourism Research, 32, 1138– 1141, 2005.
12. Kuan-Yu Chen and Cheng-Hua Wang, "Support vector regression with genetic algorithms in forecasting tourism demand," Tourism Management, pp. Volume 28, Issue 1, February 2007, Pages 215–226, 2005.
13. G. Peter Zhang and Min Qi, "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, vol. 160, no. 2, pp. 501-514, 2005.
14. Ilan Alona, Min Qi, and Robert J. Sadowski, "Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods," Journal of Retailing and Consumer Services, vol. 8, no. 3, pp. 147–156, 2001.
15. C.W.J. Granger and T. Terasvirta, Modelling Nonlinear Economic Relationships: Oxford University Press, 1993.
16. Tugba Efendigil, Semih Önüt, and Cengiz Kahraman, "A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis," Expert Systems with Applications 36 (2009) 6697–6707, 2009.
17. William Cleveland, Visualizing Data.: Hobart Press, 1993.
18. S. Haykin, Neural Networks: a comprehensive foundation., Second Edition ed.: Prentice Hall, 1999, 842p.
19. M. Tim Jones, Artificial Intelligence: A Systems Approach.: Infinity Science Press LLC, 2008.
20. Christopher M. Bishop, Neural Networks for Pattern Recognition.: Oxford University Press, 1995.
21. Atilla Aslanargun, Mammadagha Mammadov, Berna Yazici, and Senay Yolacan, "Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting," Journal of Statistical Computation and Simulation Vol. 77, No. 1, January , 29–53, 2007.
22. V. Vapnik, The nature of statistical learning theory. New York: Springer, 1995.
23. Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed.: Morgan Kaufmann, 2005.
24. Harris Drucker, Chris J.C. Burges, Linda Kaufman, Alex Smola, and Vladimir Vapnik, "Support vector regression machines," Adv. Neural Inform. Process. Syst. 9 (1997) 155–161., 1997.
25. Vojislav Kecman, Learning and Soft Computing:Support Vector Machines, Neural Networks and Fuzzy Logic Models.: A Bradford Book 1 edition ISBN-10: 0262112558, 2001.
26. Mark Hall et al., "The WEKA data mining software: an update," ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. Volume 11 Issue 1 ACM New York NY USA ISSN: 1931-0145, 2009.
27. Marko Robnik-Šikonja and Igor Kononenko, "An adaptation of Relief for attribute estimation in regression," 1997.