Skip to main content

Time Series Analysis in R

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

Start Course for Free
4 Hours16 Videos58 Exercises47,880 Learners
4600 XP

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

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.

  1. 1

    Exploratory time series data analysis

    Free

    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.

    Play Chapter Now
    Welcome to the course!
    50 xp
    Exploring raw time series
    100 xp
    Basic time series plots
    100 xp
    What does the time index tell us?
    100 xp
    Sampling frequency
    50 xp
    Identifying the sampling frequency
    100 xp
    When is the sampling frequency exact?
    50 xp
    Missing values
    100 xp
    Basic time series objects
    50 xp
    Creating a time series object with ts()
    100 xp
    Testing whether an object is a time series
    100 xp
    Plotting a time series object
    100 xp
  2. 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.

    Play Chapter Now
    Trend spotting!
    50 xp
    Random or not random?
    50 xp
    Name that trend
    50 xp
    Removing trends in variability via the logarithmic transformation
    100 xp
    Removing trends in level by differencing
    100 xp
    Removing seasonal trends with seasonal differencing
    100 xp
    The white noise (WN) model
    50 xp
    Simulate the white noise model
    100 xp
    Estimate the white noise model
    100 xp
    The random walk (RW) model
    50 xp
    Simulate the random walk model
    100 xp
    Simulate the random walk model with a drift
    100 xp
    Estimate the random walk model
    100 xp
    Stationary processes
    50 xp
    Stationary or not?
    50 xp
    Are the white noise model or the random walk model stationary?
    100 xp
  3. 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.

    Play Chapter Now
    Scatterplots
    50 xp
    Asset prices vs. asset returns
    100 xp
    Characteristics of financial time series
    100 xp
    Plotting pairs of data
    100 xp
    Covariance and correlation
    50 xp
    Calculating sample covariances and correlations
    100 xp
    Guess the correlation coefficient
    50 xp
    Autocorrelation
    50 xp
    Calculating autocorrelations
    100 xp
    The autocorrelation function
    100 xp
    Visualizing the autocorrelation function
    100 xp
  4. 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.

    Play Chapter Now
    The autoregressive model
    50 xp
    Simulate the autoregressive model
    100 xp
    Estimate the autocorrelation function (ACF) for an autoregression
    100 xp
    Persistence and anti-persistence
    50 xp
    Compare the random walk (RW) and autoregressive (AR) models
    100 xp
    AR model estimation and forecasting
    50 xp
    Estimate the autoregressive (AR) model
    100 xp
    Simple forecasts from an estimated AR model
    100 xp
  5. 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.

    Play Chapter Now
    The simple moving average model
    50 xp
    Simulate the simple moving average model
    100 xp
    Estimate the autocorrelation function (ACF) for a moving average
    100 xp
    MA model estimation and forecasting
    50 xp
    Estimate the simple moving average model
    100 xp
    Simple forecasts from an estimated MA model
    100 xp
    Compare AR and MA models
    50 xp
    AR vs MA models
    100 xp
    Name that model by time series plot
    50 xp
    Name that model by ACF plot
    50 xp
    Congratulations!
    50 xp

In the following tracks

Quantitative AnalystTime Series

Collaborators

Lore DirickMatt Isaacs

Prerequisites

Intermediate R
David S. Matteson Headshot

David S. Matteson

Associate Professor at Cornell University

David S. Matteson is Professor of Statistical Science at Cornell University and co-author of Statistics and Data Analysis for Financial Engineering with R examples.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA