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forecastSNSTS  

Forecasting for Stationary and Non-Stationary Time Series
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]
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 22 Dec. 2024.
Previous versions and publish date:
1.0-0 (2016-11-12 00:55), 1.1-0 (2016-11-17 18:07), 1.1-1 (2017-01-20 10:48), 1.2-0 (2017-06-18 19:36)
Other packages that cited forecastSNSTS R package
View forecastSNSTS citation profile
Other R packages that forecastSNSTS depends, imports, suggests or enhances
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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