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

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

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 ne...

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Detalles Bibliográficos
Autores Principales:	Rivero C.R., Pucheta J., Otaño P., Orjuela-Cañon A.D., Patiño D., Franco L., Gorrostieta E., Puglisi J.L., Juarez G.
Formato:	Objeto de conferencia (Conference Object)
Lenguaje:	Inglés (English)
Publicado:	Institute of Electrical and Electronics Engineers Inc. 2019
Materias:	Bayesian networks Forecasting Inference engines Learning systems Time series Bayesian Bayesian neural networks Computational resources Kullback Leibler divergence Kullback-Leibler information Marginal likelihood Subjective uncertainty Time series forecasting Recurrent neural networks Bayesian approximation Kullback-Leibler Divergence Recurrent neural network Time Series Forecasting
Acceso en línea:	https://repository.urosario.edu.co/handle/10336/22344 https://doi.org/10.1109/ColCACI.2019.8781984

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https://repository.urosario.edu.co/handle/10336/22344
https://doi.org/10.1109/ColCACI.2019.8781984

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

Internet

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