Time Series Forecasting using Recurrent Neural Networks modified by Bayesian Inference in the Learning Process

Cristian Rodriguez Rivero, Julian Pucheta, Paula Otaño, Alvaro David Orjuela-Cañon, Daniel Patiño, Leonardo Franco, Efren Gorrostieta, Jose Luis Puglisi, Gustavo Juarez

Resultado de la investigación: Capítulo en Libro/Reporte/ConferenciaContribución a la conferencia

2 Citas (Scopus)

Resumen

Typically, time series forecasting is done by using models based directly on the past observations from the same sequence. In these cases, when the model is learning from data, there is not an extra quantity of noiseless data available and computational resources are unlimited. In practice, it is necessary to deal with finite noisy datasets, which lead to uncertainty about what so appropriate the model is. For this, the employment of models based on Bayesian inference are preferable. Then, probabilities are treated as a way to represent the subjective uncertainty from rational agent, performing an approximated inference by maximizing a lower bound on the marginal likelihood. A modified algorithm using long-short memory recurrent neural networks for time series forecasting was presented. This new approach was chosen in order to be as close as possible to the original series in the sense of minimizing the associated Kullback-Leibler Information Criterion. A simulation study was conducted to evaluate and illustrate results, comparing this approach with Bayesian neural-networks-based algorithms for artificial chaotic time-series.

Idioma originalInglés estadounidense
Título de la publicación alojada2019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019 - Proceedings
EditoresAlvaro David Orjuela-Canon
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728116143
DOI
EstadoPublicada - jun 2019
Evento2019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019 - Barranquilla, Colombia
Duración: jun 5 2019jun 7 2019

Serie de la publicación

Nombre2019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019 - Proceedings

Conferencia

Conferencia2019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019
País/TerritorioColombia
CiudadBarranquilla
Período6/5/196/7/19

All Science Journal Classification (ASJC) codes

  • Inteligencia artificial
  • Redes de ordenadores y comunicaciones
  • Informática aplicada
  • Visión artificial y reconocimiento de patrones
  • Sistemas de información

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