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>.

Package details

AuthorTobias Kley [aut, cre], Philip Preuss [aut], Piotr Fryzlewicz [aut]
MaintainerTobias Kley <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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forecastSNSTS documentation built on Sept. 2, 2019, 5:06 p.m.