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Course

ARIMA Models in R

BasicSkill Level

4.8+

Updated 08/2024

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

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RProbability & Statistics4 hr13 videos45 Exercises3,600 XP34,732Statement of Accomplishment

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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.

Prerequisites

Time Series Analysis in R

1

Time Series Data and Models

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.

First things first

Elements of time series

Stationarity and nonstationarity

Differencing

Detrending data

Dealing with trend and heteroscedasticity

Stationary time series: ARMA

Simulating ARMA models

2

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.

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

3

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.

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

4

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.

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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ARIMA Models in R

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CHRISTIAN SNAIDER

8 hours ago

excellent course

Diego

yesterday

Anghely Sindey

2 days ago

ROBERT BRANDON

3 days ago

Muy buen programa para aprender a programar

ADERLY REYLLY

3 days ago

Contiene ejemplos claros que te ayudan a entender perfectamente el funcionamiento de los códigos

Renzo

3 days ago

"excellent course"

CHRISTIAN SNAIDER

Anghely Sindey

"Muy buen programa para aprender a programar"

ROBERT BRANDON

FAQs

Which R packages are used for fitting ARIMA models?

You primarily use the astsa package alongside the base R stats package. These provide the tools for fitting, diagnosing, and forecasting with ARMA, ARIMA, and seasonal ARIMA models.

Does the course cover seasonal time series data?

Yes. Chapter 4 is dedicated to seasonal ARIMA models, where you learn to fit and forecast seasonal patterns by extending the techniques from earlier chapters.

What prior knowledge of time series is expected?

You should complete Time Series Analysis in R along with Introduction to R and Intermediate R. These provide the time series foundations this course builds upon.

Will I work with simulated data or real-world data?

Both. You first practice fitting ARMA models to simulated data where the correct model is known, then progress to fitting ARIMA models on various real-world datasets.

How do I know if the ARIMA model I fit is valid?

The course teaches model identification, selection, and verification techniques. You learn diagnostic checks to confirm your chosen model is appropriate for the data before forecasting.

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