Package: forecastSNSTS 1.3-0

Tobias Kley

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>.

Authors:Tobias Kley [aut, cre], Philip Preuss [aut], Piotr Fryzlewicz [aut]

forecastSNSTS_1.3-0.tar.gz
forecastSNSTS_1.3-0.zip(r-4.7)forecastSNSTS_1.3-0.zip(r-4.6)forecastSNSTS_1.3-0.zip(r-4.5)
forecastSNSTS_1.3-0.tgz(r-4.6-x86_64)forecastSNSTS_1.3-0.tgz(r-4.6-arm64)forecastSNSTS_1.3-0.tgz(r-4.5-x86_64)forecastSNSTS_1.3-0.tgz(r-4.5-arm64)
forecastSNSTS_1.3-0.tar.gz(r-4.7-arm64)forecastSNSTS_1.3-0.tar.gz(r-4.7-x86_64)forecastSNSTS_1.3-0.tar.gz(r-4.6-arm64)forecastSNSTS_1.3-0.tar.gz(r-4.6-x86_64)
forecastSNSTS_1.3-0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
forecastSNSTS/json (API)
NEWS

# Install 'forecastSNSTS' in R:
install.packages('forecastSNSTS', repos = c('https://cranhaven.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tobiaskley/forecastsnsts/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

archivedpackagesr-universecpp

2.48 score 5 stars 12 scripts 55 downloads 5 exports 1 dependencies

Last updated from:1f77791c7c (on package/forecastSNSTS). Checks:13 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK130
linux-devel-x86_64OK116
source / vignettesOK182
linux-release-arm64OK106
linux-release-x86_64OK112
macos-release-arm64OK113
macos-release-x86_64OK195
macos-oldrel-arm64OK100
macos-oldrel-x86_64OK210
windows-develOK99
windows-releaseOK103
windows-oldrelOK100
wasm-releaseOK103

Exports:acfARpfMSPEpredCoeftvARMA

Dependencies:Rcpp