Recovery-Informed Forecasting Strategy Enhancement
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911476103512064 |
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| author | Li, Feng Ruan, Taozhu |
| author_facet | Li, Feng Ruan, Taozhu |
| contents | We propose a three-stage framework named as Recovery-Informed Strategy Enhancement (RISE) to forecast the recovery of Chinese outbound tourism following the coronavirus disease 2019 pandemic. The framework decomposes the forecasts into three parts: the initial forecasts, the terminal forecasts and the recovery curve forecasts that connect the two points. We integrate multiple sources of information and employ forecast combination techniques in all stages, enhancing both the accuracy and robustness of recovery forecasts. Compared with conventional forecasting approaches, our framework provides a structured and transparent pipeline to integrate model-based forecasts with expert-informed judgment under structural breaks and high uncertainty. Our findings demonstrate the effectiveness of this framework, offering an adaptable tool for recovery trajectory forecasting in post-crisis contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01085 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Recovery-Informed Forecasting Strategy Enhancement Li, Feng Ruan, Taozhu Applications Computation We propose a three-stage framework named as Recovery-Informed Strategy Enhancement (RISE) to forecast the recovery of Chinese outbound tourism following the coronavirus disease 2019 pandemic. The framework decomposes the forecasts into three parts: the initial forecasts, the terminal forecasts and the recovery curve forecasts that connect the two points. We integrate multiple sources of information and employ forecast combination techniques in all stages, enhancing both the accuracy and robustness of recovery forecasts. Compared with conventional forecasting approaches, our framework provides a structured and transparent pipeline to integrate model-based forecasts with expert-informed judgment under structural breaks and high uncertainty. Our findings demonstrate the effectiveness of this framework, offering an adaptable tool for recovery trajectory forecasting in post-crisis contexts. |
| title | Recovery-Informed Forecasting Strategy Enhancement |
| topic | Applications Computation |
| url | https://arxiv.org/abs/2603.01085 |