forecastSNSTS

Forecasting for Stationary and Non-Stationary Time Series

Methods to compute linear h-step ahead prediction coefficients based on localised and iterated Yule-Walker estimates and empirical mean squared and absolute prediction errors for the resulting predictors. Also, functions to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time series, and to verify an assumption from Kley et al. (2017), Preprint <http://personal.lse.ac.uk/kley/forecastSNSTS.pdf>.

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Description file content

Package
forecastSNSTS
Title
Forecasting for Stationary and Non-Stationary Time Series
Version
1.2-0
Description
Methods to compute linear h-step ahead prediction coefficients based on localised and iterated Yule-Walker estimates and empirical mean squared and absolute prediction errors for the resulting predictors. Also, functions to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time series, and to verify an assumption from Kley et al. (2017), Preprint .
Depends
R (>= 3.2.3)
License
GPL (>= 2)
URL
BugReports
http://github.com/tobiaskley/forecastSNSTS/issues
Encoding
UTF-8
LazyData
true
LinkingTo
Rcpp
Imports
Rcpp
Collate
'RcppExports.R' 'acfARp.R' 'f.R' 'forecastSNSTS-package.R' 'measure-of-accuracy.R' 'models.R'
RoxygenNote
6.0.1
Suggests
testthat
NeedsCompilation
yes
Packaged
2017-06-18 16:37:14 UTC; kley
Author
Tobias Kley [aut, cre], Philip Preuss [aut], Piotr Fryzlewicz [aut]
Maintainer
Tobias Kley
Repository
CRAN
Date/Publication
2017-06-18 17:36:22 UTC

install.packages('forecastSNSTS')

1.2-0

5 months ago

http://github.com/tobiaskley/forecastSNSTS

Tobias Kley

GPL (>= 2)

Depends on

R (>= 3.2.3)

Imports

Rcpp

Suggests

testthat

Discussions