Multivariate Time Series Data Imputation
Dataset Preparation for Analysis
Elapsed Time
Row Means Estimates
Internal function
Example from Johnson & Wichern's Book
Multivariate Normal Imputation
Missing Dataset Statistics
Plot the Imputed Matrix
Imputed Dataset Extraction
Print Model Output
Print Summary
Summary Information
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.