Data analysis - unemployment II

Now, you will continue fitting an SARIMA model to the monthly US unemployment unemp time series by looking at the sample ACF and PACF of the fully differenced series.

Note that the lag axis in the sample P/ACF plot is in terms of years. Thus, lags 1, 2, 3, … represent 1 year (12 months), 2 years (24 months), 3 years (36 months), …

Once again, the astsa package has been pre-loaded for you.

Diese Übung ist Teil des Kurses

ARIMA Models in R

Anleitung zur Übung

Difference the data fully (as in the previous exercise) and plot the sample ACF and PACF of the transformed data to lag 60 months (5 years). Consider that, for
- the nonseasonal component: the PACF cuts off at lag 2 and the ACF tails off.
- the seasonal component: the ACF cuts off at lag 12 and the PACF tails off at lags 12, 24, 36, …
Suggest and fit a model using sarima(). Check the residuals to ensure appropriate model fit.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Plot P/ACF pair of fully differenced data to lag 60
dd_unemp <- diff(diff(unemp), lag = 12)


# Fit an appropriate model

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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 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

Aktuelle Übung

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!