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 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 arXiv:1611.04460 <http://arxiv.org/abs/1611.04460>.

Version: 1.1-1
Depends: R (≥ 3.2.3)
Imports: Rcpp
LinkingTo: Rcpp
Suggests: testthat
Published: 2017-01-20
Author: Tobias Kley [aut, cre], Philip Preuss [aut], Piotr Fryzlewicz [aut]
Maintainer: Tobias Kley <t.kley at lse.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: NEWS
CRAN checks: forecastSNSTS results

Downloads:

Reference manual: forecastSNSTS.pdf
Package source: forecastSNSTS_1.1-1.tar.gz
Windows binaries: r-devel: forecastSNSTS_1.1-1.zip, r-release: forecastSNSTS_1.1-1.zip, r-oldrel: forecastSNSTS_1.1-1.zip
OS X Mavericks binaries: r-release: forecastSNSTS_1.1-1.tgz, r-oldrel: forecastSNSTS_1.1-1.tgz
Old sources: forecastSNSTS archive

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