Continuous Latent Adversarial Autoencoder: A Time-Sensitive Method for Incomplete Time-Series Modeling

Incomplete time-series modeling is an unavoidable topic in real-world time-series analysis because of the frequent occurrence of missing values in practical data. However, integrating data preprocessing and subsequent analysis within a model can amplify the errors from processed values. Moreover, mo...

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Vydáno v:	IEEE internet of things journal Ročník 12; číslo 7; s. 8552 - 8569
Hlavní autoři:	Chang, Zhuoqing, Liu, Shubo, Cai, Zhaohui, Tu, Guoqing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.04.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Adversarial autoencoder (AAE) adversarial training Analytical models Automation Coders Data models Decoding Differential equations Imputation incomplete time series Internet of Things Long short term memory Mathematical models Missing data Modelling Neural networks ordinary differential equation (ODE) Ordinary differential equations probabilistic forecasting Time series Time series analysis Training
ISSN:	2327-4662, 2327-4662
On-line přístup:	Získat plný text
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