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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>.
Getting started
Browse package contents
Package details |
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| Author | Tobias Kley [aut, cre], Philip Preuss [aut], Piotr Fryzlewicz [aut] |
| Maintainer | Tobias Kley <tobias.kley@bristol.ac.uk> |
| License | GPL (>= 2) |
| Version | 1.3-0 |
| URL | http://github.com/tobiaskley/forecastSNSTS |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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