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forecastSNSTS
View on CRAN: Click
here
Download and install forecastSNSTS package within the R console
Install from CRAN:
install.packages("forecastSNSTS")
Install from Github:
library("remotes")
install_github("cran/forecastSNSTS") Install by package version:
library("remotes")
install_version("forecastSNSTS", "1.3-0") Attach the package and use:
library("forecastSNSTS")
Maintained by
Tobias Kley
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-11-12
Latest Update: 2019-09-02
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. (2019), Electronic of Statistics,
forthcoming. Preprint .
How to cite:
Tobias Kley (2016). forecastSNSTS: Forecasting for Stationary and Non-Stationary Time Series. R package version 1.3-0, https://cran.r-project.org/web/packages/forecastSNSTS. Accessed 15 Jul. 2026.
Previous versions and publish date:
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Complete documentation for forecastSNSTS
Functions, R codes and Examples using
the forecastSNSTS R package
Some associated functions: acfARp . computeMSPEcpp . f . forecastSNSTS-package . measure-of-accuracy . plot.measure-of-accuracy . predCoef . ts-models-tvARMA . tvARMAcpp .
Some associated R codes: RcppExports.R . acfARp.R . f.R . forecastSNSTS-package.R . measure-of-accuracy.R . models.R . Full forecastSNSTS package functions and examples
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