ARIMA Modeling with R

January 19, 2017
By

(This article was first published on DataCamp Blog, and kindly contributed to R-bloggers)

We just launched ARIMA Modeling with R taught by David Stoffer. David Stoffer is a Professor of Statistics at the University of Pittsburgh. He is also a 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. In this course, you will become an expert in fitting ARIMA models to time series data using R. Ready to get started? 

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ARIMA Modeling with R features 45 interactive exercises that combine high-quality video, in-browser coding, and gamification for an engaging learning experience that will get you on your way to becoming an ARIMA model expert!

What you’ll learn

In the first chapter, 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. [Start First Chapter For Free] Next, 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 also learn how to use R time series commands from the stats and astsa packages. In the third chapter, you will learn about integrated ARMA (ARIMA) models for nonstationary time series. Here you will fit the models to real data using R time series commands from the stats and astsa packages. In the final chapter, 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.

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