Addressing model uncertainty in probabilistic forecasting using Monte Carlo dropout

Cristián Serpell, Ignacio A. Araya, Carlos Valle, Héctor Allende

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In recent years, deep learning models have been developed to address probabilistic forecasting tasks, assuming an implicit stochastic process that relates past observed values to uncertain future values. These models are capable of capturing the inherent uncertainty of the underlying process, but they ignore the model uncertainty that comes from the fact of not having infinite data. This work proposes addressing the model uncertainty problem using Monte Carlo dropout, a variational approach that assigns distributions to the weights of a neural network instead of simply using fixed values. This allows to easily adapt common deep learning models currently in use to produce better probabilistic forecasting estimates, in terms of their consideration of uncertainty. The proposal is validated for prediction intervals estimation on seven energy time series, using a popular probabilistic model called Mean Variance Estimation (MVE), as the deep model adapted using the technique.

Original languageEnglish
Pages (from-to)S185-S205
JournalIntelligent Data Analysis
Issue numberS1
StatePublished - 2020
Externally publishedYes


  • Probabilistic forecasting
  • epistemic uncertainty
  • mean variance estimation method
  • monte carlo dropout
  • prediction interval


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