Time Series Analysis: Forecasting and Control

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Prentice Hall, 1994 - Mathematics - 598 pages
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.

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Contents

INTRODUCTION
1
Stochastic Models and Their Forecasting
19
Link Between the Sample Spectrum
44
Copyright

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About the author (1994)

GEORGE BOX, PHD, DSC, FRS, is R. A. Fisher Professor Emeritus of Statistics and Industrial Engineering at the University of Wisconsin. He has been the director of research for investigators at Imperial Chemical Industries, Princeton University, and University of Wisconsin-Madison. He is a Fellow of the Royal Society of London and the American Academy of Arts and Sciences, and an Honorary Member and Shewhart and Deming Medalist of the American Society for Quality. He was awarded the Samuel S. Wilks Memorial Medal from the American Statistical Association and the Guy Medal in Gold from the Royal Statistical Society. He is the author of more than 200 published papers and more than a dozen critically acclaimed books.

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