Time Series Forecasting with Missing Values

Time Series Forecasting with Missing Values

Time series prediction has become more popular in various kinds of applications such as weather prediction, control engineering, financial analysis, industrial monitoring, etc. To deal with real-world problems, we are often faced with missing values in the data due to sensor malfunctions or human er...

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Bibliographic Details
Main Authors:	Shin-Fu Wu, Chia-Yung Chang, Shie-Jue Lee
Format:	Article
Language:	English
Published:	European Alliance for Innovation (EAI) 2015-11-01
Series:	EAI Endorsed Transactions on Cognitive Communications
Subjects:	time series prediction missing values local time index least squares support vector machine (lssvm)
Online Access:	http://eudl.eu/doi/10.4108/icst.iniscom.2015.258269

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