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
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forecastSNSTS_1.2-0.9000.tar.gz(r-4.5-noble)forecastSNSTS_1.2-0.9000.tar.gz(r-4.4-noble)
forecastSNSTS_1.2-0.9000.tgz(r-4.4-emscripten)forecastSNSTS_1.2-0.9000.tgz(r-4.3-emscripten)
forecastSNSTS.pdf |forecastSNSTS.html
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 234 downloads 5 exports 1 dependencies

Last updated 7 years agofrom:484fed0c1f. Checks:1 OK, 11 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 04 2025
R-4.5-win-x86_64NOTEMar 04 2025
R-4.5-mac-x86_64NOTEMar 04 2025
R-4.5-mac-aarch64NOTEMar 04 2025
R-4.5-linux-x86_64NOTEMar 04 2025
R-4.4-win-x86_64NOTEMar 04 2025
R-4.4-mac-x86_64NOTEMar 04 2025
R-4.4-mac-aarch64NOTEMar 04 2025
R-4.4-linux-x86_64NOTEMar 04 2025
R-4.3-win-x86_64NOTEMar 04 2025
R-4.3-mac-x86_64NOTEMar 04 2025
R-4.3-mac-aarch64NOTEMar 04 2025

Exports:acfARpfMSPEpredCoeftvARMA

Dependencies:Rcpp