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A decomposition-ensemble approach for tourism forecasting
| Content Provider | Scilit |
|---|---|
| Author | Xie, Gang Qian, Yatong Wang, Shouyang |
| Copyright Year | 2020 |
| Description | Journal: Annals of Tourism Research 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. |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147863/pdf |
| Ending Page | 102891 |
| Page Count | 1 |
| Starting Page | 102891 |
| ISSN | 01607383 |
| DOI | 10.1016/j.annals.2020.102891 |
| Journal | Annals of Tourism Research |
| Volume Number | 81 |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2020-02-25 |
| Access Restriction | Open |
| Subject Keyword | Journal: Annals of Tourism Research Tourism, Leisure, Sport and Hospitality Tourism Demand Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Data Characteristic Analysis Time Series Forecasting |
| Content Type | Text |
| Resource Type | Article |
| Subject | Business and International Management Tourism, Leisure and Hospitality Management Development Marketing |
