Titlebook: Bayesian Forecasting and Dynamic Models; Mike West,Jeff Harrison Book 19891st edition Springer Science+Business Media New York 1989 data a

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Liang Lin,Dongyu Zhang,Ping Luo,Wangmeng Zuore the second-order models, also sometimes referred to as linear growth models, that much of this chapter is concerned with. Higher order polynomial models are also discussed for completeness, although it is rare that polynomials of order greater than three are required for practice. The structure o

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Euzebiusz Jamrozik,Michael J. Selgelidl components are a useful first attack. If retrospective analysis is the primary goal, then these simple and purely descriptive models may be adequate in themselves, providing estimates of the trend (or deseasonalised series), seasonal pattern (detrended series) and irregular or random component ove

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Jacques Berleur,Tanguy Ewbank de Wespined models in Chapter 10 where deterioration in forecasting performance, though small, is apparent. In this Chapter, we move closer to illustrating forecasting systems rather than simply models, considering ways in which routine interventions can be incorporated into existing DLMs, and examples of wh

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Book 19891st edition writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea­ sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mat

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