Forecast-Then-Optimize Deep Learning Methods

• URL: http://arxiv.org/abs/2506.13036v1
• Date: Mon, 16 Jun 2025 02:02:30 GMT
• Title: Forecast-Then-Optimize Deep Learning Methods
• Authors: Jinhang Jiang, Nan Wu, Ben Liu, Mei Feng, Xin Ji, Karthik Srinivasan,
• Abstract summary: Time series forecasting underpins vital decision-making across various sectors, yet raw predictions from sophisticated models often harbor systematic errors and biases.<n>We examine the Forecast-Then-Then (FTO) framework, pioneering its systematic synopsis.<n>Deep learning and large language models have established superiority over traditional parametric forecasting models for most enterprise applications.
• Score: 10.067896857251162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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