With a focus on analyzing and modeling linear dynamic systems using
statistical methods, Time Series Analysis formulates various linear
models, discusses their theoretical characteristics, and explores
the connections among stochastic dynamic models. Emphasizing the
time domain description, the author presents theorems to highlight
the most important results, proofs to clarify some results, and
problems to illustrate the use of the results for modeling
real-life phenomena.
The book first provides the formulas and methods needed to adapt
a second-order approach for characterizing random variables as well
as introduces regression methods and models, including the general
linear model. It subsequently covers linear dynamic deterministic
systems, stochastic processes, time domain methods where the
autocorrelation function is key to identification, spectral
analysis, transfer-function models, and the multivariate linear
process. The text also describes state space models and recursive
and adaptivemethods. The final chapter examines a host of practical
problems, including the predictions of wind power production and
the consumption of medicine, a scheduling system for oil delivery,
and the adaptive modeling of interest rates.
Concentrating on the linear aspect of this subject, Time Series
Analysis provides an accessible yet thorough introduction to the
methods for modeling linear stochastic systems. It will help you
understand the relationship between linear dynamic systems and
linear stochastic processes.
General
| Imprint: |
Chapman & Hall/CRC
|
| Country of origin: |
United States |
| Series: |
Chapman & Hall/CRC Texts in Statistical Science |
| Release date: |
November 2007 |
| First published: |
November 2007 |
| Authors: |
Henrik Madsen
|
| Dimensions: |
234 x 156 x 27mm (L x W x T) |
| Format: |
Hardcover
|
| Pages: |
400 |
| ISBN-13: |
978-1-4200-5967-0 |
| Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
| LSN: |
1-4200-5967-X |
| Barcode: |
9781420059670 |
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