## 4.2 Notes on TSA in `R`

and `Matlab`

If you use `R`

the command below will install all the packages we will use during the entire course on you private computer. This might take too long on a university PC, just install the packages you need for an assignment each session.

```
install.packages(c("devtools", "rio", "plyr", "dplyr", "tidyr", "Matrix",
"ggplot2", "lattice", "latticeExtra", "grid", "gridExtra", "rgl",
"fractal", "nonlinearTseries", "crqa",
"signal", "sapa", "ifultools", "pracma",
"nlme", "lme4", "lmerTest", "minpack.lm",
"igrpah","qgrap","graphicalVAR","bootGraph","IsingSampler","IsingFit"),
dependencies = TRUE)
```

There is also a function library you need to `source`

, the most recent version is on Github, use the `devtools`

library to source the latest online version, or just follow the link, save as an `.R`

file from your browser and open it in `R`

and source it.

```
library(devtools)
source_url("https://raw.githubusercontent.com/FredHasselman/DCS/master/functionLib/nlRtsa_SOURCE.R")
```

### 4.2.1 Importing data in `R`

If you have package `rio`

installed in `R`

, you can load the data directly into the local environment.

- Follow the link, e.g. for
`series.sav`

. - On the Github page, find a button marked
**Download**(or**Raw**for textfiles). - Copy the
`url`

associated with the**Download**button on Github (right-clik). - The copied path should contain the word ‘raw’ somewhere in the url.
- Call
`import(url)`

:

`series <- import("https://github.com/FredHasselman/DCS/raw/master/assignmentData/BasicTSA_arma/series.sav")`

You can use the function `arima()`

, `acf()`

and `pacf()`

in `R`

(`Matlab`

has functions that go by slightly different names, check the Matlab Help pages).

There are many extensions to these linear models, check the `CRAN Task View`

on `Time Series Analysis`

to learn more (e.g. about package `zoo`

and `forecast`

).