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Forecasting tourist arrivals by using the adaptive network-based fuzzy inference system.
Miao-Sheng Chen1, Li-Chih Ying2, Mei-Chiu Pan1
1Department of Business Administration, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622, Taiwan, ROC.
Expert Systems with Applications
|April 15, 2020
Summary
Accurate tourist arrival forecasting is crucial for tourism planning. The adaptive network-based fuzzy inference system (ANFIS) model shows superior performance compared to other methods for forecasting arrivals to Taiwan.
Area of Science:
- Tourism Management
- Artificial Intelligence
- Time Series Analysis
Background:
- Accurate forecasting of tourist arrivals is essential for effective tourism demand planning and infrastructure development.
- Various forecasting methods exist, but their performance can vary significantly.
Purpose of the Study:
- To apply the adaptive network-based fuzzy inference system (ANFIS) model for forecasting tourist arrivals to Taiwan.
- To evaluate and demonstrate the forecasting performance of the ANFIS model.
Main Methods:
- Adaptive Network-Based Fuzzy Inference System (ANFIS) model application.
- Comparative analysis with Fuzzy Time Series, Grey Forecasting, and Markov Residual Modified models.
- Evaluation using Mean Absolute Percentage Errors and statistical results.
Main Results:
- The ANFIS model demonstrated superior forecasting accuracy compared to the other evaluated models.
- Statistical results confirmed the enhanced performance of ANFIS for tourist arrival prediction.
- ANFIS successfully forecasted monthly tourist arrivals from key international markets.
Conclusions:
- The ANFIS model is a highly promising and effective tool for forecasting tourist arrivals.
- ANFIS offers a significant improvement over traditional forecasting methods in the tourism sector.
- The model's accuracy supports better strategic planning in tourism management.