ARIMA - plug and play

As you saw in the video, a time series is called ARIMA(\(p,d,q\)) if the differenced series (of order \(d\)) is ARMA(\(p,q\)).

To get a sense of how the model works, you will analyze simulated data from the integrated model $$ Y_t = .9 Y_{t-1} + W_t\, $$ where \(Y_t = \nabla X_t = X_t - X_{t-1}\). In this case, the model is an ARIMA(1,1,0) because the differenced data are an autoregression of order one.

The simulated time series is in x and it was generated in R as
x <- arima.sim(model = list(order = c(1, 1, 0), ar = .9), n = 200).

You will plot the generated data and the sample ACF and PACF of the generated data to see how integrated data behave. Then, you will difference the data to make it stationary. You will plot the differenced data and the corresponding sample ACF and PACF to see how differencing makes a difference.

As before, the astsa package has been pre-loaded for you. Data from an ARIMA(1,1,0) with AR parameter .9 is saved in object x.

Deze oefening maakt deel uit van de cursus

ARIMA Models in R

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Oefeninstructies

Plot the generated data.
Use acf2() from astsa to plot the sample P/ACF pair for the generated data.
Plot the differenced data.
Use another call to acf2() to view the sample P/ACF pair for the differenced data. Note how they imply an AR(1) model for the differenced data.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Plot x


# Plot the P/ACF pair of x


# Plot the differenced data


# Plot the P/ACF pair of the differenced data

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

ARIMA Models in R

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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 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

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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!