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A Hybrid ARIMA and RBF Neural Network Model for Tourist Quantity Forecasting : A Case Study for Chiangmai Province

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Rati Wongsathan and Wararat Jaroenwiriyapap

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Applications of a single model may not be able to capture different data patterns well

enough, especially in the tourist forecast problem which is often complex in nature. An

autoregressive integrated moving average (ARIMA) is a famous linear model while an

artificial neural network (ANN) is a promising alternative to a traditional linear

method. The ARIMA model may not be adequate for nonlinear problems while ANN can

well reveal the correlation of nonlinear patterns. However, overfitting due to a learning

process is the main disadvantage of ANN as well as being trapped in a local optimum

for parameters optimization. To improve the forecast performance of both ARIMA and

ANN for high accuracy, the two hybridization models, i.e. hybrid ARIMA-RBFNN

model and hybrid RBFNN-ARIMA model are employed to examine the Chiangmai’s

tourist time series data. Statistics test and parameter designed experiments were used to

optimize these models and the sum-square of error (SSE) was used to indicate their

performances. In this case study, the hybrid RBFNN-ARIMA model has proved that the

RBFNN can priori capture the non-stationary non-linear component while the fully

linearly stationary residuals were accurately predicted by ARIMA. The experimental results

demonstrated that the hybrid RBFNN-ARIMA model outperformed 42% by averaging over

the hybrid ARIMA-RBFNN model, an improvement of hybrid ARIMA-RBFNN model,

RBFNN model, and ARIMA model.

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Radial Basis Function Neural Networks; ARIMA; Hybrid RBFNN-ARIMA; Hybrid ARIMA-RBFNN