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.