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wjunger
mtsdi: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.
Maintained by Washington Junger. Last updated 3 years ago.
1 stars 4.00 score 22 scripts 3 dependents
cran
meboot:Maximum Entropy Bootstrap for Time Series
Maximum entropy density based dependent data bootstrap. An algorithm is provided to create a population of time series (ensemble) without assuming stationarity. The reference paper (Vinod, H.D., 2004 <DOI: 10.1016/j.jempfin.2003.06.002>) explains how the algorithm satisfies the ergodic theorem and the central limit theorem.
Maintained by Fred Viole. Last updated 2 years ago.
2 stars 3.38 score 2 dependents