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.

Diese Übung ist Teil des Kurses

ARIMA Models in R

Anleitung zur Übung

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.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# View a detailed description of AirPassengers


# Plot AirPassengers


# Plot the DJIA daily closings


# Plot the Southern Oscillation Index

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

ARIMA Models in R

Mittlere SchwierigkeitSchwierigkeitsgrad

4.9+

Kurs kostenlos starten

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.

Exercise 1: First things first Exercise 2: Data play

Aktuelle Übung

Exercise 3: Elements of time series Exercise 4: Stationarity and nonstationarity Exercise 5: Differencing Exercise 6: Detrending data Exercise 7: Dealing with trend and heteroscedasticity Exercise 8: Stationary time series: ARMA Exercise 9: Simulating 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.

Exercise 1: AR and MA models Exercise 2: Fitting an AR(1) model Exercise 3: Fitting an AR(2) model Exercise 4: Fitting an MA(1) model Exercise 5: AR and MA together Exercise 6: Fitting an ARMA model Exercise 7: Identify an ARMA model Exercise 8: Model choice and residual analysis Exercise 9: Model choice - I Exercise 10: Model choice - II Exercise 11: Residual analysis - I Exercise 12: Residual analysis - II Exercise 13: ARMA get in

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.

Exercise 1: ARIMA - integrated ARMA Exercise 2: ARIMA - plug and play Exercise 3: Simulated ARIMA Exercise 4: Global warming Exercise 5: ARIMA diagnostics Exercise 6: Diagnostics - simulated overfitting Exercise 7: Diagnostics - global temperatures Exercise 8: Forecasting ARIMA Exercise 9: Forecasting simulated ARIMA Exercise 10: Forecasting global temperatures

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.

Exercise 1: Pure seasonal models Exercise 2: P/ACF of pure seasonal models Exercise 3: Fit a pure seasonal model Exercise 4: Mixed seasonal models Exercise 5: Fit a mixed seasonal model Exercise 6: Data analysis - unemployment I Exercise 7: Data analysis - unemployment II Exercise 8: Data analysis - commodity prices Exercise 9: Data analysis - birth rate Exercise 10: Forecasting seasonal ARIMA Exercise 11: Forecasting monthly unemployment Exercise 12: How hard is it to forecast commodity prices?Exercise 13: Congratulations!