- 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

# 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()
[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.

```
> 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:

```
> sp_ts <- ts(sp, start=2016, frequency=365)
```

### Plot the series

Let's now plot the time series using the `plot.ts()`

function.

```
> 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.