Skip to main content

Course

Time Series Analysis in R

IntermediateSkill Level

4.8+

Updated 01/2026

Learn the core techniques necessary to extract meaningful insights from time series data.

Start Course for Free

RProbability & Statistics

4 hr

16 videos

58 Exercises

4,600 XP

61,025

Statement of Accomplishment

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies

Training a Team?

Try for Business

Course Description

Many phenomena in our day-to-day lives, such as the movement of stock prices, are measured in intervals over a period of time. Time series analysis methods are extremely useful for analyzing these special data types. In this course, you will be introduced to some core time series analysis concepts and techniques.

Prerequisites

1

Exploratory time series data analysis

This chapter will give you insights on how to organize and visualize time series data in R. You will learn several simplifying assumptions that are widely used in time series analysis, and common characteristics of financial time series.

Welcome to the course!

Exploring raw time series

Basic time series plots

What does the time index tell us?

Sampling frequency

Identifying the sampling frequency

When is the sampling frequency exact?

Missing values

Basic time series objects

Creating a time series object with ts()

Testing whether an object is a time series

Plotting a time series object

2

Predicting the future

In this chapter, you will conduct some trend spotting, and learn the white noise (WN) model, the random walk (RW) model, and the definition of stationary processes.

Trend spotting!

Random or not random?

Name that trend

Removing trends in variability via the logarithmic transformation

Removing trends in level by differencing

Removing seasonal trends with seasonal differencing

The white noise (WN) model

Simulate the white noise model

Estimate the white noise model

The random walk (RW) model

Simulate the random walk model

Simulate the random walk model with a drift

Estimate the random walk model

Stationary processes

Stationary or not?

Are the white noise model or the random walk model stationary?

3

Correlation analysis and the autocorrelation function

In this chapter, you will review the correlation coefficient, use it to compare two time series, and also apply it to compare a time series with its past, as an autocorrelation. You will discover the autocorrelation function (ACF) and practice estimating and visualizing autocorrelations for time series data.

Scatterplots

Asset prices vs. asset returns

Characteristics of financial time series

Plotting pairs of data

Covariance and correlation

Calculating sample covariances and correlations

Guess the correlation coefficient

Autocorrelation

Calculating autocorrelations

The autocorrelation function

Visualizing the autocorrelation function

4

Autoregression

In this chapter, you will learn the autoregressive (AR) model and several of its basic properties. You will also practice simulating and estimating the AR model in R, and compare the AR model with the random walk (RW) model.

The autoregressive model

Simulate the autoregressive model

Estimate the autocorrelation function (ACF) for an autoregression

Persistence and anti-persistence

Compare the random walk (RW) and autoregressive (AR) models

AR model estimation and forecasting

Estimate the autoregressive (AR) model

Simple forecasts from an estimated AR model

5

A simple moving average

In this chapter, you will learn the simple moving average (MA) model and several of its basic properties. You will also practice simulating and estimating the MA model in R, and compare the MA model with the autoregressive (AR) model.

The simple moving average model

Simulate the simple moving average model

Estimate the autocorrelation function (ACF) for a moving average

MA model estimation and forecasting

Estimate the simple moving average model

Simple forecasts from an estimated MA model

Compare AR and MA models

AR vs MA models

Name that model by time series plot

Name that model by ACF plot

Congratulations!

Time Series Analysis in R

Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance reviewEnroll Now

Don’t just take our word for it

*4.8

from 84 reviews

90%

7%

1%

1%

0%

Sort by

julio

3 days ago

Hanno

4 days ago

Andrés Emilio

2 weeks ago

Siuuu

Nitin

3 weeks ago

Umar

3 weeks ago

Muhammad

4 weeks ago

julio

Hanno

"Siuuu"

Andrés Emilio

FAQs

Who will benefit from this course?

Jobs such as Data Analysts, Financial Analysts and Marketers, who need to analyze large amounts of time-based data, would benefit from this course.

What is the Autocorrelation Function?

The Autocorrelation Function (ACF) is a statistic used to describe the correlation between a time series and its past values. It is often used to determine whether a given time series is stationary (i.e. is evolving in a predictable manner).

Will I receive a certificate at the end of the course?

Yes, upon completion of the course you will receive a DataCamp certificate to recognize your achievement.

Join over 19 million learners and start Time Series Analysis in R today!

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Grow your data skills with DataCamp for Mobile

Make progress on the go with our mobile courses and daily 5-minute coding challenges.