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:
> setwd("C:/Users/Manish/Dropbox/Finance Train/Courses/Data") > getwd()  "C:/Users/Manish/Dropbox/Finance Train/Courses/Data" > list.files()  "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.
> 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
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(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.
- Financial Time Series Data
- Exploring Time Series Data in R
- Plotting Time Series in R
- Handling Missing Values in Time Series
- Creating a Time Series Object in R
- Check if an object is a time series object in R
- Plotting Financial Time Series Data (Multiple Columns) in R
- Characteristics of Time Series
- Stationary Process in Time Series
- Transforming a Series to Stationary
- Time Series Transformation in R
- Differencing and Log Transformation
- Autocorrelation in R
- Time Series Models
- ARIMA Modeling
- Simulate White Noise (WN) in R
- Simulate Random Walk (RW) in R
- AutoRegressive (AR) Model in R
- Estimating AutoRegressive (AR) Model in R
- Forecasting with AutoRegressive (AR) Model in R
- Moving Average (MA) Model in R
- Estimating Moving Average (MA) Model in R
- ARIMA Modelling in R
- ARIMA Modelling - Identify Model for a Time Series
- Forecasting with ARIMA Modeling in R - Case Study
- Automatic Identification of Model Using auto.arima() Function in R
- Financial Time Series in R - Course Conclusion