  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.
