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  3. ARIMA Models in R
  • 1

    Time Series Data and Models

    Gratuito

    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.

  • 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.

  • 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.

  • 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.

Initializing

Exercicio

Data play

In the video, you saw various types of data. In this exercise, you will plot additional time series data and compare them to what you saw in the video. It is useful to think about how these time series compare to the series in the video. In particular, concentrate on the type of trend, seasonality or periodicity, and homoscedasticity.

Before you use a data set for the first time, you should use the help system to see the details of the data. For example, use help(AirPassengers) or ?AirPassengers to see the details of the series.

Instruções

100 XP
  • The packages astsa and xts are preloaded in your R environment.
  • Use help() to view the specifics of the AirPassengers data file.
  • Use plot() to plot the airline passenger data (AirPassengers) and compare it to a series you saw in the video.
  • Plot the DJIA daily closings (djia$Close) and compare it to a series you saw in the video.
  • Plot the Southern Oscillation Index (soi) and inspect it for trend, seasonality, and homoscedasticity.