A data-driven approach for a class of stochastic dynamic optimization problems

Thuener Silva, Davi Valladão, Tito Homem-de-Mello

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Dynamic stochastic optimization models provide a powerful tool to represent sequential decision-making processes. Typically, these models use statistical predictive methods to capture the structure of the underlying stochastic process without taking into consideration estimation errors and model misspecification. In this context, we propose a data-driven prescriptive analytics framework aiming to integrate the machine learning and dynamic optimization machinery in a consistent and efficient way to build a bridge from data to decisions. The proposed framework tackles a relevant class of dynamic decision problems comprising many important practical applications. The basic building blocks of our proposed framework are: (1) a Hidden Markov Model as a predictive (machine learning) method to represent uncertainty; and (2) a distributionally robust dynamic optimization model as a prescriptive method that takes into account estimation errors associated with the predictive model and allows for control of the risk associated with decisions. Moreover, we present an evaluation framework to assess out-of-sample performance in rolling horizon schemes. A complete case study on dynamic asset allocation illustrates the proposed framework showing superior out-of-sample performance against selected benchmarks. The numerical results show the practical importance and applicability of the proposed framework since it extracts valuable information from data to obtain robustified decisions with an empirical certificate of out-of-sample performance evaluation.

Original languageEnglish
Pages (from-to)687-729
Number of pages43
JournalComputational Optimization and Applications
Issue number3
StatePublished - Dec 2021
Externally publishedYes


  • Distributionally robust dynamic optimization
  • Hidden Markov models
  • Risk constraints
  • Stochastic dual dynamic programming
  • Stochastic programming


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