Author: Xie, Gang; Qian, Yatong; Wang, Shouyang
Title: A decomposition-ensemble approach for tourism forecasting Cord-id: vioymdp0 Document date: 2020_3_31
ID: vioymdp0
Snippet: Abstract With the frequent occurrence of irregular events in recent years, the tourism industry in some areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic analysis, and the Elman's neural network model. Using Hong Kong tourism demand as an empirical case, this study firstly inves
Document: Abstract With the frequent occurrence of irregular events in recent years, the tourism industry in some areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic analysis, and the Elman's neural network model. Using Hong Kong tourism demand as an empirical case, this study firstly investigates how data characteristic analysis is used in a decomposition-ensemble approach. The empirical results show that the proposed model outperforms other models in both point and interval forecasts for different prediction horizons, indicating the effectiveness of the proposed approach for forecasting tourism demand, especially for time series with complexity.
Search related documents:
Co phrase search for related documents- absolute percentage error and accurate forecast: 1, 2, 3
- absolute percentage error and accurate prediction: 1, 2, 3, 4
- absolute percentage error and acute respiratory syndrome: 1, 2, 3, 4
- accuracy level and activation function: 1
- accuracy level and acute respiratory syndrome: 1, 2, 3, 4, 5, 6, 7
- accuracy measurement and actual value: 1
- accuracy measurement and acute respiratory syndrome: 1, 2, 3, 4, 5, 6, 7
- accurate point and acute respiratory syndrome: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
- accurate prediction and actual value: 1
- accurate prediction and acute respiratory syndrome: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
- accurate prediction and adaptive noise: 1
- accurate prediction and adaptive noise mode decomposition: 1
- accurate tourism demand forecasting and acute respiratory syndrome: 1
- activation function and acute respiratory syndrome: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
- actual value and acute respiratory syndrome: 1
Co phrase search for related documents, hyperlinks ordered by date