This is a complete revision of a classic, seminal, and  authoritative book that has been the model for most books on the topic  written since 1970. It focuses on practical techniques throughout, rather  than a rigorous mathematical treatment of the subject.  It  explores the building of stochastic (statistical) models for time series and  their use in important areas of application —forecasting, model  specification, estimation, and checking, transfer function modeling of  dynamic relationships, modeling the effects of intervention events, and  process control. Features sections on: recently developed methods for  model specification, such as canonical correlation analysis and the use  of model selection criteria; results on testing for unit root nonstationarity  in ARIMA processes; the state space representation of ARMA models and  its use for likelihood estimation and forecasting; score test for model  checking; and deterministic components and structural components  in time series models and their estimation based on regression-time  series model methods.

Time Series Analysis: Forecasting & Control (3rd Edition) PDF

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