Multivariate Time Series Data Imputation

This is an EM algorithm based method for imputation of missing values in multivariate normal time series. The imputation algorithm accounts for both spatial and temporal correlation structures. Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region.


Reference manual

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0.3.5 by Washington Junger, 3 years ago

Browse source code at

Authors: Washington Junger <[email protected]> and Antonio Ponce de Leon <[email protected]>

Documentation:   PDF Manual  

Task views: Time Series Analysis

GPL (>= 2) license

Depends on utils, stats, gam, splines

Imported by ForecastComb, GeomComb, smartR.

See at CRAN