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ARMA Modeling and Forecasting

Part of the Springer Series in Statistics book series (SSS)

Abstract

Fitting an appropriate ARMA(p, q) model to an observed time series data set involves two interrelated problems, namely determining the order (p, q) (which is usually referred to as model identification) and estimating parameters in the model. Further, the postfitting diagnostic checking on the validity of the fitted model is equally important.

Keywords

Bayesian Information Criterion Akaike Information Criterion Maximum Likelihood Estimator Standardize Residual ARMA Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Sciences+Business Media, Inc. 2005

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