Chapter 10. Stationary Time Series Study Notes contains 28 pages covering the following learning objectives:

* Describe the requirements for a series to be covariance stationary.

* Define the autocovariance function and the autocorrelation function.

* Define white noise, describe independent white noise and normal (Gaussian) white noise.

* Define and describe the properties of autoregressive (AR) processes.

* Define and describe the properties of moving average (MA) processes.

* Explain how a lag operator works.

* Explain mean reversion and calculate a mean-reverting level.

* Define and describe the properties of autoregressive moving average (ARMA) processes.

* Describe the application of AR, MA, and ARMA processes.

* Describe sample autocorrelation and partial autocorrelation.

* Describe the Box-Pierce Q-statistic and the Ljung-Box Q statistic.

* Explain how forecasts are generated from ARMA models.

* Describe the role of mean reversion in long-horizon forecasts.

* Explain how seasonality is modeled in a covariance-stationary ARMA.

After reviewing these notes you will be able to apply what you learned with practice questions.

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