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Kalman Filter with Missing Data

The Kalman filter with missing data extends the classical Kalman filter to handle time series in which some observations are absent. When an observation is missing at time t the update step is skipped and the state estimate is carried forward from the prediction step alone. Combined with the Expectation-Maximisation (EM) algorithm, the approach also estimates unknown model parameters from incomplete data, making it a practical tool for real-world irregularly observed series.

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Kalman Filter for State-Space Models with Missing Observations

Dossier de méthode taxonomique · bayesian / bayesian

Shumway, R. H. & Stoffer, D. S. (2000). Time Series Analysis and Its Applications. Springer. · ISBN 978-0387989501
Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. · ISBN 978-0521405737

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Méthodes apparentées

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Taxonomic bucketBayesian Inference with Missing Datamachine-suggested · Relational suggestion, not evidence.See alsoEM Algorithmmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketKalman Filtermachine-suggested · Relational suggestion, not evidence.Taxonomic bucketParticle Filter with Missing Datamachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSequential Monte Carlomachine-suggested · Relational suggestion, not evidence.See alsoState Space Modelmachine-suggested · Relational suggestion, not evidence.