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ARIMA Algebra
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The goal of Chapter 2 is to derive the properties of common processes and, based on these properties, to develop a general scheme for classifying processes. Stationary processes includes white noise, moving average (MA), and autoregressive (AR) processes. MA and AR models can approximate mixed ARMA models. A lag or backshift operator is used to solve ARIMA models for time series observations or random shocks. Covariance functions are derived for each of the common processes.Maximum likelihood estimates are introduced for the purposes of estimating autoregressive and moving average parameters.
Oxford University Press
Title: ARIMA Algebra
Description:
The goal of Chapter 2 is to derive the properties of common processes and, based on these properties, to develop a general scheme for classifying processes.
Stationary processes includes white noise, moving average (MA), and autoregressive (AR) processes.
MA and AR models can approximate mixed ARMA models.
A lag or backshift operator is used to solve ARIMA models for time series observations or random shocks.
Covariance functions are derived for each of the common processes.
Maximum likelihood estimates are introduced for the purposes of estimating autoregressive and moving average parameters.
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