• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
Finance Train

Finance Train

High Quality tutorials for finance, risk, data science

  • Home
  • Data Science
  • CFA® Exam
  • PRM Exam
  • Tutorials
  • Careers
  • Products
  • Login

Differencing and Log Transformation

Data Science, Statistics

This lesson is part 12 of 27 in the course Financial Time Series Analysis in R

Removing Variability Using Logarithmic Transformation

Since the data shows changing variance over time, the first thing we will do is stabilize the variance by applying log transformation using the log() function. The resulting series will be a linear time series.

> sp_linear<-log(sp_ts)
> plot.ts(sp_linear, main="Daily Stock Prices (log)", ylab="Price", col=4)

Removing Linear Trend

We will now perform the first difference transformation [z(t) - z(t-1)] to our series to remove the linear trend.

> sp_linear_diff <- diff(sp_linear)
> plot.ts(sp_linear_diff, main="Daily Stock Prices (log)", ylab="Price", col=4)
>

Removing Seasonal Differencing

Let’s take another example to understand how we can use the diff() function to remove seasonal differencing from data. We will use the John Deer’s Quarterly earnings data we used earlier as it exhibits seasonality.

> par(mfrow = c(1,2))
> de_earnings_diff <- diff(johndeere_earnings,lag=4)
> plot.ts(johndeere_earnings, main="Earnings (Quarterly)")
> plot.ts(de_earnings_diff, main="Earnings (Differenced, lag=4)")

The chart on the left shows the original earnings. The chart on the right shows the difference in earnings with a lag of 4.

Previous Lesson

‹ Time Series Transformation in R

Next Lesson

Autocorrelation in R ›

Join Our Facebook Group - Finance, Risk and Data Science

Posts You May Like

How to Improve your Financial Health

CFA® Exam Overview and Guidelines (Updated for 2021)

Changing Themes (Look and Feel) in ggplot2 in R

Coordinates in ggplot2 in R

Facets for ggplot2 Charts in R (Faceting Layer)

Reader Interactions

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Primary Sidebar

In this Course

  • 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

Latest Tutorials

    • Data Visualization with R
    • Derivatives with R
    • Machine Learning in Finance Using Python
    • Credit Risk Modelling in R
    • Quantitative Trading Strategies in R
    • Financial Time Series Analysis in R
    • VaR Mapping
    • Option Valuation
    • Financial Reporting Standards
    • Fraud
Facebook Group

Membership

Unlock full access to Finance Train and see the entire library of member-only content and resources.

Subscribe

Footer

Recent Posts

  • How to Improve your Financial Health
  • CFA® Exam Overview and Guidelines (Updated for 2021)
  • Changing Themes (Look and Feel) in ggplot2 in R
  • Coordinates in ggplot2 in R
  • Facets for ggplot2 Charts in R (Faceting Layer)

Products

  • Level I Authority for CFA® Exam
  • CFA Level I Practice Questions
  • CFA Level I Mock Exam
  • Level II Question Bank for CFA® Exam
  • PRM Exam 1 Practice Question Bank
  • All Products

Quick Links

  • Privacy Policy
  • Contact Us

CFA Institute does not endorse, promote or warrant the accuracy or quality of Finance Train. CFA® and Chartered Financial Analyst® are registered trademarks owned by CFA Institute.

Copyright © 2021 Finance Train. All rights reserved.

  • About Us
  • Privacy Policy
  • Contact Us