On the Impact of Deep Learning-based Time-series Forecasts on Multistage Stochastic Programming Policies

TL;DR

This paper explores how deep learning-based probabilistic time-series forecasting improves multistage stochastic programming policies by providing more accurate uncertainty modeling, leading to better decision-making in complex, uncertain environments.

Contribution

It introduces a deep learning-based forecasting method into multistage stochastic programming and compares its effectiveness against traditional methods in a practical setting.

Findings

Deep learning forecasts significantly improve model performance.

More accurate forecasts enable high-quality solutions with simple heuristics.

Probabilistic forecasting enhances scenario sampling and risk prediction.

Abstract

Multistage stochastic programming provides a modeling framework for sequential decision-making problems that involve uncertainty. One typically overlooked aspect of this methodology is how uncertainty is incorporated into modeling. Traditionally, statistical forecasting techniques with simple forms, e.g., (first-order) autoregressive time-series models, are used to extract scenarios to be added to optimization models to represent the uncertain future. However, often times, the performance of these forecasting models are not thoroughly assessed. Motivated by the advances in probabilistic forecasting, we incorporate a deep learning-based time-series forecasting method into multistage stochastic programming framework, and compare it with the cases where a traditional forecasting method is employed to model the uncertainty. We assess the impact of more accurate forecasts on the quality of two commonly used look-ahead policies, a deterministic one and a two-stage one, in a rolling\red{-}horizon framework on a practical problem. Our results illustrate that more accurate forecasts contribute substantially to the model performance, and enable obtaining high-quality solutions even from computationally cheap heuristics. They also show that the probabilistic forecasting capabilities of deep learning-based methods can be especially beneficial when used as a (conditional) sampling tool for scenario-based models, and to predict the worst-case scenario for risk-averse models.