Forecast-Then-Optimize Deep Learning Methods

TL;DR

This paper reviews the Forecast-Then-Optimize framework, highlighting how it improves decision-making by refining forecasts with optimization techniques, especially using deep learning and large language models, across various sectors.

Contribution

It systematically synthesizes advancements in deep learning FTO architectures from 2016 to 2025, emphasizing their role in enhancing forecast accuracy and decision robustness.

Findings

FTO improves predictive accuracy over traditional methods.

Deep learning models outperform parametric forecasting in enterprise applications.

FTO enhances decision robustness and operational efficiency.

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.