| forecastSNSTS-package {forecastSNSTS} | R Documentation |
Forecasting of Stationary and Non-Stationary Time Series
Description
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).
Details
| Package: | forecastSNSTS |
| Type: | Package |
| Version: | 1.3-0 |
| Date: | 2019-09-02 |
| License: | GPL (>= 2) |
Contents
The core functionality of this R package is accessable via the function
predCoef, which is used to compute the linear prediction
coefficients, and the functions MSPE and MAPE,
which are used to compute the empirical mean squared or absolute prediction
errors. Further, the function f can be used to verify
condition (10) of Theorem 3.1 in Kley et al. (2019) for any given tvAR(p) model.
The function tvARMA can be used to simulate time-varying
ARMA(p,q) time series.
The function acfARp computes the autocovariances of a AR(p)
process from the coefficients and innovations standard deviation.
Author(s)
Tobias Kley
References
Kley, T., Preuss, P. & Fryzlewicz, P. (2019). Predictive, finite-sample model choice for time series under stationarity and non-stationarity. Electronic Journal of Statistics, forthcoming. [cf. https://arxiv.org/abs/1611.04460]