R TimeSeries

In this section we will demonstrate how to do basic univariate time-series modelling with R. We will use a package written by Rob Hyndman, called "forecast". So before you get started you need to go to R and

   install.packages("forecast")

But note that this package requires R of version 3. Then at the beginning of your code you will have to import the library by adding

   library(forecast)

to your code.

Importing Data

Here we will initially use a dataset on UK CPI UKCPI.xls. Doenload this and save it as a csv file as this will facilitate the upload to R.

   setwd("YOUR DIRECTORY")              # This sets the working directory
   data <- read.csv("UKCPI.csv")  # Opens UKCPI.csv from working directory

which will produce a dataframe (

data

) with two variables, one giving dates (

DATE

) and the other containing the actual CPI data for 1988Q1 to 2013Q4 (

UKCPI

).

Basic Time-Series Data Transformations

The first thing we need is to ensure that R knows that the data we are dealing with are actually time series data. There is a function in R you can use to check that.

Additional Resources

• A very quick intro from Quick-R can be found here [1]
• We are using the package "forecast" authored by Rob Hyndman who has also written an online textbook on the topic of forecasting [2]
• To access some very useful data-series in a very convenient way we will also use the QUANDL package.