Handbook of Time Series Analysis, Signal Processing, and Dynamics (Signal Processing and its Applications)
| Author | : | |
| Rating | : | 4.25 (682 Votes) |
| Asin | : | 0125609906 |
| Format Type | : | paperback |
| Number of Pages | : | 848 Pages |
| Publish Date | : | 2015-02-05 |
| Language | : | English |
DESCRIPTION:
A Customer said Broad Mathematical Background. I found this book to be mainly interesting for those researchers or practicioners who find themselves diving into the river of time series analysis and try to survive not having a very strong mathematical background. This book should allow them to apply the most classic mathematical tools such as time-domain and spectral-domain techniques to their time series problems (provided they are "relatively non-complex") and also to gain . "Excellent Reference Book for Engineers & Statisticians" according to A Customer. I have been using this book for several months now and its a great reference book for the applied statistician and engineer. I got this book hoping to get pseudo-code for standard time-series analysis, fourier transforms, etc. In addition to the algorithms, I was extremely impressed by the clear explanations of difficult mathematical concept and descriptions of the numerical methods used. I'm now using this as a text to educate m. Good for the starters in time-series analysis Well written, this book would be good for the practitioners with engineering background and without rigorous mathematics. The coverage is rather conventional or classical in the sense that intelligent appoaches are not touched.
The aim of this book is to serve as a graduate text and reference in time series analysis and signal processing, two closely related subjects that are the concern of a wide range of disciplines, such as statistics, electrical engineering, mechanical engineering and physics.The book provides a CD-ROM containing codes in PASCAL and C for the computer procedures printed in the book. It also furnishes a complete program devoted to the statistical analysis of time series, which will be attractive to a wide range of academics working in diverse mathematical disciplines.