CRAN/E | forecastSNSTS

forecastSNSTS

Forecasting for Stationary and Non-Stationary Time Series

Installation

About

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 .

github.com/tobiaskley/forecastSNSTS
Bug report File report

Key Metrics

Version 1.3-0
R ≥ 3.2.3
Published 2019-09-02 1553 days ago
Needs compilation? yes
License GPL-2
License GPL-3
CRAN checks forecastSNSTS results

Downloads

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Last 7 days 51 +50%
Last 30 days 189 +1%
Last 90 days 512 -9%
Last 365 days 2.456 -34%

Maintainer

Maintainer

Tobias Kley

tobias.kley@bristol.ac.uk

Authors

Tobias Kley

aut / cre

Philip Preuss

aut

Piotr Fryzlewicz

aut

Material

README
NEWS
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

forecastSNSTS archive

Depends

R ≥ 3.2.3

Imports

Rcpp

Suggests

testthat

LinkingTo

Rcpp