Time Series Transformation in R
We will now learn about how we can perform the mathematical transformations in R in order to make a non-stationary series stationary.
We have the daily stock prices of an imaginary stock exhibiting rapid growth stored in a csv file. You can download the csv file and follow along this tutorial to perform the transformations in your R console. There are two hundred observations starting from 1st Jan , 2016.
We have placed the csv file in our working directory as shown below:
1> setwd("C:/Users/Manish/Dropbox/Finance Train/Courses/Data")
2> getwd()
3[1] "C:/Users/Manish/Dropbox/Finance Train/Courses/Data"
4> list.files()
5 [1] "stock_daily.csv"
6
As you can see, the file stock_daily.csv
in available in our working directory.
Load Data in R
We will read this csv file in R using read.csv()
function and store the dataset in a variable called sp
. The data will be stored as a dataframe.
1> sp <- read.csv("stock_daily.csv", header=FALSE)
2>
3> str(sp)
4'data.frame': 200 obs. of 1 variable:
5 $ V1: num 254 229 277 262 264 ...
6>
7
Convert dataframe to time series using ts()
function
Now that we have the data in a dataframe, we can convert it into a time series object using the ts()
function as shown below:
> sp_ts <- ts(sp, start=2016, frequency=365)
Plot the series
Let's now plot the time series using the plot.ts()
function.
1> plot.ts(sp_ts,col=4, main="Daily Stock Prices", ylab="Price")
2

We can observe two things here:
- There is a clearly visible upward trend in the data
- The variability in the data is increasing over time.
Let's now look at how we can remove both these patterns to stationarize the series.