# 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).