Recovery-Informed Forecasting Strategy Enhancement

2026-03-01

AI Summary

This study addresses the heightened forecasting challenges in modeling the post-pandemic recovery trajectory of outbound tourism from China, which has been shaped by structural breaks and profound uncertainty. To tackle this complexity, the authors propose a three-stage RISE framework that decomposes the recovery process into an initial value, a terminal value, and the transitional recovery curve linking them. By integrating multi-source data, ensemble forecasting techniques, and expert judgment, the framework establishes a structured and transparent prediction mechanism. Empirical results demonstrate that this approach substantially enhances both predictive accuracy and robustness. Moreover, the RISE framework offers a generalizable modeling tool for tourism recovery in post-crisis contexts, delivering notable methodological innovation and practical utility for policymakers and industry stakeholders.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Uncertainty Representations
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

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.

Problem

• recovery forecasting
• outbound tourism
• post-crisis recovery
• structural breaks
• forecast uncertainty

Innovation

• Recovery-Informed Forecasting
• Three-Stage Framework
• Forecast Combination
• Structural Breaks
• Expert Judgment Integration