Probabilistic Forecasting Using Monte Carlo Dropout Neural Networks

Cristián Serpell, IGNACIO DANIEL ARAYA ZAMORANO, Carlos Valle, Héctor Allende

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Using artificial neural networks for forecasting tasks is a popular approach that has proven to be very accurate. When used to estimate prediction intervals, a normal distribution is usually assumed as the data noise uncertainty term, as in MVE networks, while model parameters uncertainty is often ignored. Because of this, prediction intervals estimated by them are narrow in uncertain regions where train data is scarce. To tackle this problem we apply Monte Carlo dropout, which is a model uncertainty representation technique, to the network parameters of a Long Short-Term Memory MVE network, allowing us to construct better prediction intervals in probabilistic forecasting tasks. We compare our proposal with the pure MVE method in four wind speed and one consumer load real forecasting datasets, showing that our method improves results in terms of the Winkler loss in both one step ahead and multi-step ahead probabilistic forecasting.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 24th Iberoamerican Congress, CIARP 2019, Proceedings
EditorsIngela Nyström, Yanio Hernández Heredia, Vladimir Milián Núñez
PublisherSpringer
Pages387-397
Number of pages11
ISBN (Print)9783030339036
DOIs
StatePublished - 2019
Externally publishedYes
Event24th Iberoamerican Congress on Pattern Recognition, CIARP 2019 - Havana, Cuba
Duration: 28 Oct 201931 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11896 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th Iberoamerican Congress on Pattern Recognition, CIARP 2019
CountryCuba
CityHavana
Period28/10/1931/10/19

Keywords

  • Consumer load forecasting
  • Mean Variance Estimation
  • Monte Carlo dropout
  • Prediction interval
  • Probabilistic forecasting
  • Wind speed forecasting

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