An innovative regime switching model to forecast Taiwan tourism demand

The tourism industry has become a major part of economic development for many countries. These countries have greatly invested in tourism to attract more tourist arrivals. Hence, the need for more accurate forecasts of tourism demand is important. Various approaches have been applied to forecast tourism demand of different countries. However, tourism demands tend to be imprecise and their trends nonlinear. In addition, there may be drastic changes in the tourism demand time series. To properly handle these problems, this study proposes an innovative forecasting model to detect the regime switching properly and to apply fuzzy time-series model to forecast. The monthly tourist arrivals to Taiwan will be used as forecasting target. The analysis by the proposed model will be validated by the major events as well as previous studies.

[1]  Hyun Jeong Kim,et al.  Tourism expansion and economic development: The case of Taiwan , 2005, Tourism Management.

[2]  Jinhyung Chon,et al.  A forecasting model of tourist arrivals from major markets to Thailand. , 2003 .

[3]  Shyi-Ming Chen,et al.  Forecasting enrollments based on fuzzy time series , 1996, Fuzzy Sets Syst..

[4]  Woo Gon Kim,et al.  The impact of macroeconomic and non-macroeconomic forces on hotel stock returns , 2004, International Journal of Hospitality Management.

[5]  Chaohui Wang,et al.  Predicting tourism demand using fuzzy time series and hybrid grey theory. , 2004 .

[6]  C. Witt,et al.  Forecasting tourism demand: A review of empirical research , 1995 .

[7]  L. Moutinho,et al.  Modeling and forecasting tourism demand: the case of flows from Mainland China to Taiwan , 2008 .

[8]  Jen-Hung Huang,et al.  Earthquake devastation and recovery in tourism: the Taiwan case , 2002 .

[9]  Kunhuang Huarng,et al.  A dynamic approach to adjusting lengths of intervals in fuzzy time series forecasting , 2004, Intell. Data Anal..

[10]  M. Munday,et al.  The contribution of tourism to the UK economy: Satellite account perspectives , 2006 .

[11]  L. Moutinho,et al.  An Advanced Approach to Forecasting Tourism Demand in Taiwan , 2007 .

[12]  Kun-Huang Huarng,et al.  A bivariate fuzzy time series model to forecast the TAIEX , 2008, Expert Syst. Appl..

[13]  Kunhuang Huarng,et al.  Ratio-Based Lengths of Intervals to Improve Fuzzy Time Series Forecasting , 2006, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Kun-Huang Huarng,et al.  The application of neural networks to forecast fuzzy time series , 2006 .

[15]  R. Law.  Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting , 2000 .

[16]  Kun-Huang Huarng,et al.  A Multivariate Heuristic Model for Fuzzy Time-Series Forecasting , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  Haiyan Song,et al.  Forecasting international tourist flows to Macau , 2006 .

[18]  Kunhuang Huarng,et al.  Effective lengths of intervals to improve forecasting in fuzzy time series , 2001, Fuzzy Sets Syst..

[19]  Fong-Lin Chu,et al.  Forecasting tourism: a combined approach , 1998 .

[20]  Alan M. Sykes,et al.  Forecasting international conference attendance , 1995 .

[21]  Rob Law,et al.  A practitioners guide to time-series methods for tourism demand forecasting - a case study of Durban, South Africa , 2001 .

[22]  James D. Hamilton.  Regime switching models , 2010 .

[23]  Kunhuang Huarng,et al.  Heuristic models of fuzzy time series for forecasting , 2001, Fuzzy Sets Syst..

[24]  W. Woodall,et al.  A comparison of fuzzy forecasting and Markov modeling , 1994 .

[25]  E. Smeral.  Long-term forecasts for tourism industries: the case of Austria and Switzerland. , 1992 .