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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.
Time Series Data and ModelsFree
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.
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 models50 xpFitting an AR(1) model100 xpFitting an AR(2) model100 xpFitting an MA(1) model100 xpAR and MA together50 xpFitting an ARMA model100 xpIdentify an ARMA model50 xpModel choice and residual analysis50 xpModel choice - I100 xpModel choice - II50 xpResidual analysis - I100 xpResidual analysis - II50 xpARMA get in100 xp
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 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 models50 xpP/ACF of pure seasonal models50 xpFit a pure seasonal model100 xpMixed seasonal models50 xpFit a mixed seasonal model100 xpData analysis - unemployment I100 xpData analysis - unemployment II100 xpData analysis - commodity prices100 xpData analysis - birth rate100 xpForecasting seasonal ARIMA50 xpForecasting monthly unemployment100 xpHow hard is it to forecast commodity prices?100 xpCongratulations!50 xp
PrerequisitesTime Series Analysis in R
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.