Interactive Course

ARIMA Modeling with R

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

4 hours
13 Videos
45 Exercises
12,688 Participants
3,600 XP

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Course Description

In this course, you will become an expert in fitting ARIMA models to time series data using R. First, you will explore the nature of time series data using the tools in the R stats package. Next, you learn how to fit various ARMA models to simulated data (where you will know the correct model) using the R package astsa. Once you have mastered the basics, you will learn how to fit integrated ARMA models, or ARIMA models to various real data sets. You will learn how to check the validity of an ARIMA model and you will learn how to forecast time series data. Finally, you will learn how to fit ARIMA models to seasonal data, including forecasting using the astsa package.

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

Course Outline

You will investigate the nature of time series data and learn the basics of ARMA models that can explain the behavior of such data. You will learn the basic R commands needed to help set up raw time series data to a form that can be analyzed using ARMA models.

Now that you know how to fit ARMA models to stationary time series, you will learn about integrated ARMA (ARIMA) models for nonstationary time series. You will fit the models to real data using R time series commands from the stats and astsa packages.

You will discover the wonderful world of ARMA models and how to fit these models to time series data. You will learn how to identify a model, how to choose the correct model, and how to verify a model once you fit it to data. You will learn how to use R time series commands from the stats and astsa packages.

You will learn how to fit and forecast seasonal time series data using seasonal ARIMA models. This is accomplished using what you learned in the previous chapters and by learning how to extend the R time series commands available in the stats and astsa packages.

1
Time Series Data and Models
Free

You will investigate the nature of time series data and learn the basics of ARMA models that can explain the behavior of such data. You will learn the basic R commands needed to help set up raw time series data to a form that can be analyzed using ARMA models.
View Chapter Details Play Chapter Now
Fitting ARMA models

You will discover the wonderful world of ARMA models and how to fit these models to time series data. You will learn how to identify a model, how to choose the correct model, and how to verify a model once you fit it to data. You will learn how to use R time series commands from the stats and astsa packages.
View Chapter Details Play Chapter Now
ARIMA Models

Now that you know how to fit ARMA models to stationary time series, you will learn about integrated ARMA (ARIMA) models for nonstationary time series. You will fit the models to real data using R time series commands from the stats and astsa packages.
View Chapter Details Play Chapter Now
Seasonal ARIMA

You will learn how to fit and forecast seasonal time series data using seasonal ARIMA models. This is accomplished using what you learned in the previous chapters and by learning how to extend the R time series commands available in the stats and astsa packages.
View Chapter Details Play Chapter Now

David Stoffer

Professor of Statistics at the University of Pittsburgh

David Stoffer is a Professor of Statistics at the University of Pittsburgh. He is member of the editorial board of the Journal of Time Series Analysis and Journal of Forecasting. David is the coauthor of the book "Time Series Analysis and Its Applications: With R Examples", which is the basis of this course. Another (free) book he wrote on Time Series Analysis is available here.

Course Instructor

David Stoffer

Professor of Statistics at the University of Pittsburgh

Collaborator(s)

Lore Dirick
Matt Isaacs

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ARIMA Modeling with R

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Course Description

Course Outline

Time Series Data and Models

First Things First

Data Play

Elements of Time Series

Stationarity and Nonstationarity

Differencing

Detrending Data

Dealing with Trend and Heteroscedasticity

Stationary Time Series: ARMA

Simulating ARMA Models

ARIMA Models

ARIMA - Integrated ARMA

ARIMA - Plug and Play

Simulated ARIMA

Global Warming

ARIMA Diagnostics

Diagnostics - Simulated Overfitting

Diagnostics - Global Temperatures

Forecasting ARIMA

Forecasting Simulated ARIMA

Forecasting Global Temperatures

Fitting ARMA models

AR and MA Models

Fitting an AR(1) Model

Fitting an AR(2) Model

Fitting an MA(1) Model

AR and MA together

Fitting an ARMA model

Identify an ARMA Model

Model Choice and Residual Analysis

Model Choice - I

Model Choice - II

Residual Analysis - I

Residual Analysis - II

ARMA get in

Seasonal ARIMA

Pure Seasonal Models

P/ACF of Pure Seasonal Models

Fit a Pure Seasonal Model

Mixed Seasonal Models

Fit a Mixed Seasonal Model

Data Analysis - Unemployment I

Data Analysis - Unemployment II

Data Analysis - Commodity Prices

Data Analysis - Birth Rate

Forecasting Seasonal ARIMA

Forecasting Monthly Unemployment

How Hard is it to Forecast Commodity Prices?

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