# forecastSNSTS: Forecasting of Stationary and Non-Stationary Time Series

The `forecastSNSTS`

package provides methods to compute linear h-step prediction coefficients based on localised and iterated Yule-Walker estimates and empirical mean square prediction errors from the resulting predictors.

It is intended to support the paper Predictive, finite-sample model choice for time series under stationarity and non-stationarity, which we refer to as Kley et al. (2019).

You can track (and contribute to) the development of `forecastSNSTS`

at https://github.com/tobiaskley/forecastSNSTS. If you encounter unexpected behaviour while using `forecastSNSTS`

, please write an email or file an issue.

## Getting started with `forecastSNSTS`

First, if you have not done so already, install R from http://www.r-project.org (click on download R, select a location close to you, and download R for your platform). Once you have the latest version of R installed and started execute the following commands on the R shell:

```
install.packages("forecastSNSTS")
devtools::install_github("tobiaskley/forecastSNSTS", ref="develop")
```

This will first install the R package `devtools`

and then use it to install the latest (development) version of `forecastSNSTS`

from the GitHub repository. In case you do not have LaTeX installed on your computer you may want to use

Now that you have R and `forecastSNSTS`

installed you can access all the functions available. To load the package and access the help files:

```
library(forecastSNSTS)
help("forecastSNSTS")
```

A demo is available. It can be started by

`demo("tvARMA11")`

At the bottom of the online help page to the package you will find an index to all the help files available.

## Replicating the examples of the paper with `forecastSNSTSexamples`

Note that there is a separate R package, called forecastSNSTSexamples and available only on GitHub, that can be used to replicate the empirical examples from Section 5 of Kley et al. (2019).