Package: forecastSNSTS 1.2-0.9000

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. (2017), Preprint <http://personal.lse.ac.uk/kley/forecastSNSTS.pdf>.

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

forecastSNSTS_1.2-0.9000.tar.gz
forecastSNSTS_1.2-0.9000.zip(r-4.7)forecastSNSTS_1.2-0.9000.zip(r-4.6)forecastSNSTS_1.2-0.9000.zip(r-4.5)
forecastSNSTS_1.2-0.9000.tgz(r-4.6-x86_64)forecastSNSTS_1.2-0.9000.tgz(r-4.6-arm64)forecastSNSTS_1.2-0.9000.tgz(r-4.5-x86_64)forecastSNSTS_1.2-0.9000.tgz(r-4.5-arm64)
forecastSNSTS_1.2-0.9000.tar.gz(r-4.7-arm64)forecastSNSTS_1.2-0.9000.tar.gz(r-4.7-x86_64)forecastSNSTS_1.2-0.9000.tar.gz(r-4.6-arm64)forecastSNSTS_1.2-0.9000.tar.gz(r-4.6-x86_64)
forecastSNSTS_1.2-0.9000.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://tobiaskley.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:

cpp

3.40 score 5 stars 9 scripts 180 downloads 5 exports 1 dependencies

Last updated from:484fed0c1f. Checks:11 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE104
linux-devel-x86_64NOTE113
source / vignettesOK165
linux-release-arm64NOTE101
linux-release-x86_64NOTE114
macos-release-arm64NOTE158
macos-release-x86_64NOTE263
macos-oldrel-arm64NOTE149
macos-oldrel-x86_64NOTE267
windows-develNOTE116
windows-releaseNOTE99
windows-oldrelNOTE93
wasm-releaseOK103

Exports:acfARpfMSPEpredCoeftvARMA

Dependencies:Rcpp