Abstract
Daily tourism demand forecasting can provide important implications for the tourism industry. However, there exist limitations in applying traditional methods to forecast daily tourism demand because of the complex seasonal patterns and holiday effects. In this study, we introduce FB Prophet and apply it to the forecasting of daily tourism demand in the Jiuzhai Valley National Park and Macao from Mainland China. The decomposition result of FB Prophet shows its ability to handle the influence of seasonal patterns and holiday effects. The forecasting results show that considering seasonal patterns, holiday effects, and other predictors can significantly improve the forecasting performance, and FB Prophet outperforms other methods.
| Original language | English |
|---|---|
| Pages (from-to) | 1573-1592 |
| Number of pages | 20 |
| Journal | Current Issues in Tourism |
| Volume | 26 |
| Issue number | 10 |
| Online published | 14 Apr 2022 |
| DOIs | |
| Publication status | Published - 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth
Research Keywords
- Tourism demand forecasting
- daily tourism demand
- holiday effects
- seasonal patterns
- FB Prophet
- EMPIRICAL MODE DECOMPOSITION
- TIME-SERIES
- NEURAL-NETWORK
- OCCUPANCY
- ACCURACY
- TRAVEL
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