用优化方法改进深度学习预测，让决策更准更稳。
Forecast-Then-Optimize Deep Learning Methods
- 先用深度学习预测，再通过优化调整偏差和不确定性
- 2016-2025年主流架构被系统分析，提升预测鲁棒性
- 适合运管领域做精准决策的从业者参考
时间序列预测在多个领域支撑关键决策，但复杂模型的原始输出常含系统性误差与偏差。本文系统综述了「预测后优化」（Forecast-Then-Optimize, FTO）框架，区别于传统「预测后优化」（PTO）方法，FTO 显式利用集成方法、元学习器及不确定性校准等优化技术提升预测质量。深度学习与大语言模型在多数企业应用中已超越传统参数化模型。本研究梳理了2016至2025年间的重要进展，分析主流深度学习型FTO架构。聚焦运管领域的实际应用，验证了FTO在提高预测准确性、鲁棒性与决策有效性方面的关键作用。研究为未来预测方法提供理论与实践结合的基准指导。
原文摘要 · Abstract (English)
Time series forecasting underpins vital decision-making across various sectors, yet raw predictions from sophisticated models often harbor systematic errors and biases. We examine the Forecast-Then-Optimize (FTO) framework, pioneering its systematic synopsis. Unlike conventional Predict-Then-Optimize (PTO) methods, FTO explicitly refines forecasts through optimization techniques such as ensemble methods, meta-learners, and uncertainty adjustments. Furthermore, deep learning and large language models have established superiority over traditional parametric forecasting models for most enterprise applications. This paper surveys significant advancements from 2016 to 2025, analyzing mainstream deep learning FTO architectures. Focusing on real-world applications in operations management, we demonstrate FTO's crucial role in enhancing predictive accuracy, robustness, and decision efficacy. Our study establishes foundational guidelines for future forecasting methodologies, bridging theory and operational practicality.
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