Time Series Forecasting with Missing Data Using Generative Adversarial Networks and Bayesian Inference

Time Series Forecasting with Missing Data Using Generative Adversarial Networks and Bayesian Inference

This paper tackles the challenge of time series forecasting in the presence of missing data. Traditional methods often struggle with such data, which leads to inaccurate predictions. We propose a novel framework that combines the strengths of Generative Adversarial Networks (GANs) and Bayesian infer...

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Bibliographic Details
Main Author: Xiaoou Li
Format: Article
Language:English
Published: MDPI AG 2024-04-01
Series:Information
Subjects:
time series
missing data
neural networks
GAN
Bayesian
Online Access:https://www.mdpi.com/2078-2489/15/4/222

Internet

https://www.mdpi.com/2078-2489/15/4/222

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