Influence of Missing Values on the Prediction of a Stationary Time Series

DOI10.1111/j.1467-9892.2005.00433.xzbMath1091.62094OpenAlexW2149621509MaRDI QIDQ5467615

Pascal Bondon

Publication date: 24 May 2006

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.2005.00433.x

zbMATH Keywords

asymptotic behaviourprediction theorylong-memory processesmissing value problem

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (8)

Optimal linear interpolation of multiple missing values ⋮ Some prediction problems for stationary random fields with quarter-plane past ⋮ Estimation error for blind Gaussian time series prediction ⋮ Duals of random vectors and processes with applications to prediction problems with missing values ⋮ Filtering of multidimensional stationary sequences with missing observations ⋮ Assessing influence in Gaussian long-memory models ⋮ Interpolation of stationary sequences observed with a noise ⋮ Prediction of stationary Gaussian random fields with incomplete quarterplane past

Cites Work

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• Methodologies for the estimation of missing observations in time series
• Time series: theory and methods.
• Prediction with incomplete past and interpolation of missing values
• Prediction with incomplete past of a stationary process.
• Fractional ARIMA with stable innovations
• Prediction Variance and Information Worth of Observations in Time Series
• ESTIMATION AND INTERPOLATION OF MISSING VALUES OF A STATIONARY TIME SERIES

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