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. (2019), Electronic of Statistics, forthcoming. Preprint <arXiv:1611.04460>.

Version: 1.3-0
Depends: R (≥ 3.2.3)
Imports: Rcpp
LinkingTo: Rcpp
Suggests: testthat
Published: 2019-09-02
Author: Tobias Kley [aut, cre], Philip Preuss [aut], Piotr Fryzlewicz [aut]
Maintainer: Tobias Kley <tobias.kley at bristol.ac.uk>
BugReports: http://github.com/tobiaskley/forecastSNSTS/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://github.com/tobiaskley/forecastSNSTS
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: forecastSNSTS results

Downloads:

Reference manual: forecastSNSTS.pdf
Package source: forecastSNSTS_1.3-0.tar.gz
Windows binaries: r-devel: forecastSNSTS_1.3-0.zip, r-release: forecastSNSTS_1.3-0.zip, r-oldrel: forecastSNSTS_1.3-0.zip
OS X binaries: r-release: forecastSNSTS_1.3-0.tgz, r-oldrel: forecastSNSTS_1.3-0.tgz
Old sources: forecastSNSTS archive

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