混合模式於觀光需求預測之研究

A Novelty Hybrid Forecast Model to Forecast Tourism Demand

10.29916/JMPP.201109.0005

張育維(Yu-Wei Chang)

觀光預測 ； 季節性ARIMA模式 ； 類神經網路 ； 混合預測 ； tourism forecasting ； seasonal ARIMA ； artificial neural network ； hybrid forecast

管理實務與理論研究

5卷3期（2011 / 09 / 01）

74 - 86

繁體中文

預測在發展觀光中扮演了重要的角色，過去研究中發現SARIMA與類神經網路是兩種常被使用的預測模式，兩者各有優缺點，亦有各種不同的使用環境，如：SARIMA模式適用在線性預測，類神經網路適用在非線性預測。由於觀光數列的組成常會受到許多因素所影響，且常包括線性和非線性成分，不容易進行預測，有必要發展出一套合適的程序進行觀光需求預測。爲改進過去觀光需求預測上的缺點，本研究提出一套結合SARIMA模式與類神經網路模式組合而成的混合預測模式用以預測觀光需求。本研究以臺灣地區2000年1月～2010年12月入境觀光資料做爲模式構建和驗證使用。研究結果發現SARIMA模式之預測績效優於類神經預測模式。在三種預測模式中，混合預測模式誤差最小，顯示混合模式改進了SARIMA模式的缺點，提高了預測效益。本研究所使用的混合預測模式，將可改傳統上預測模式的缺點，以提供決策者作爲預測未來觀光需求使用。

During the past decades, the seasonal ARIMA model (SARIMA) is one of the widely used linear models in tourism forecasting. More recently, the artificial neural networks (ANNs) have been used as an alternative to the traditional linear approaches. ARIMA and ANN models are often compared with different conclusions in forecasting performance. Tourism time series often consists of complex linear and non-linear patterns and difficult to forecast. In this research, a novel hybrid approach combining both the SARIMA and ANNs models is proposed to forecast the tourism demand in Taiwan. The hybrid model would be able to strength the unique use of ARIMA and ANN models in linear and nonlinear modeling. Monthly time series data covering from 2000.01 to 2010.12 are used in this research. The mean absolute percentage error (MAPE) is used to compare the performance of the hybrid model against other two models (i.e., the SARIMA model and the ANNs model). Results show that the hybrid forecast outperforms among the three models.

1. 郭英峰、陳邦誠(2007)。以單變量ARIMA 模式、類神經網路與灰色GM（1,1）模型預測高雄港貨櫃吞吐量。台大管理論叢，17(2)，107-132。
   連結：
2. 世界觀光組織：http://www.unwto.org/index.php。
3. Box, G. E. P.,Jenkins, G. M.(1970).Time Series Analysis, Forecasting and Control.San Franciso:Holden Day.
4. Chen, Ching-Fu,Chang, Yu-Hern,Chang, Yu-Wei(2009).Seasonal ARIMA forecasting of inbound air travel arrivals to Taiwan.Transportmetrica,5(2),125-140.
5. Cho, V.(2003).A comparison of three different approaches to tourist arrival forecasting.Tourism Management,24,323-330.
6. Coshall, C.(2006).Time series analyses of UK outbound travel by air.Journal of Travel Research,44,335-347.
7. Lewis, C. D.(1982).International and Business Forecasting Methods.London:Butterworths.
8. Lim, C.,McAlee, M.(2002).Time series forecasts of international travel demand for Austrila.Tourism Management,23(4),389-396.
9. Lim, C.,Pan, G. W.(2005).Inbound tourism developments and patterns in China.Mathematics and Computers in Simulation,68,499-507.
10. Martin, C. A.,Witt, S. F.(1989).Forecasting tourism demand: a comparison of the accuracy several quantitative methods.International Journal of Forecasting,5,7-19.
11. Pankratz, A.(1983).Forecasting with univariate Box-Jenkins method.NY:Wiley.
12. Petropoulosa, C.,Nikolopoulosb, K.,Patelisa, A.,Assimakopoulos, V.(2005).A technical analysis approach to tourism demand forecasting.Applied Economics Letters,12,327-333.
13. Pilar, G.,Moral, P.(1995).An Analysis of the international tourism demand in Spain.International Journal of Forecasting,11,233-251.
14. Yeung, M.,Law, R.(2005).Forecasting US Air Travelers to Europe, Caribbean and Asia.Asia Pacific Journal of Tourism Research,10(2),137-149.
15. Zhang, C. P.(2003).Time series forecasting using a hybrid ARIMA and neural network model.Neurocomputing,50,159-175.
16. 李响、宗群、童玲(2006)。汽車銷售混合預測方法研究。天津大學學報，8(3)，175-178。
17. 林茂文(2006)。時間數列分析與預測。臺北:華泰文化。
18. 葉怡成(2004)。應用類神經網路。臺北:儒林出版社。
19. 詹錢登、曾志民、王志賢、王啟明(2006)。類神經網路應用於颱風報潮之預測。海洋工程學刊，6(1)，1-24。

1. (2024)。基於需求預測與應用對臺灣觀光產業品質分析的選擇研究。品質學報，31(3)，151-177。