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:
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> setwd("C:/Users/Manish/Dropbox/Finance Train/Courses/Data") > getwd() [1] "C:/Users/Manish/Dropbox/Finance Train/Courses/Data" > list.files() [1] "stock_daily.csv" |
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.
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> sp <- read.csv("stock_daily.csv", header=FALSE) > > str(sp) 'data.frame': 200 obs. of 1 variable: $ V1: num 254 229 277 262 264 ... > |
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:
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> sp_ts <- ts(sp, start=2016, frequency=365) |
Plot the series
Let’s now plot the time series using the plot.ts() function.
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> plot.ts(sp_ts,col=4, main="Daily Stock Prices", ylab="Price") |
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.
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