Posted by **ChrisRedfield** at Nov. 15, 2013

Published: 2006-05-25 | ISBN: 0387293175 | PDF | 575 pages | 5 MB

Posted by **Nice_smile)** at Jan. 13, 2017

English | 2007 | ISBN: 0199228876 | 240 Pages | PDF | 1.05 MB

Posted by **libr** at Dec. 15, 2016

English | 2008 | ISBN: 0471653977 | 472 pages | PDF | 4 MB

Posted by **alt_f4** at Oct. 27, 2016

English | Apr. 24, 2006 | ISBN: 044451838X | 461 Pages | PDF | 4 MB

The business cycle has long been the focus of empirical economic research. Until recently, statistical analysis of macroeconomic fluctuations was dominated by linear time series methods.

Posted by **fdts** at Sept. 20, 2016

by Manfred Mudelsee

English | 2014 | ISBN: 3319044494 | 454 pages | PDF | 10.57 MB

Posted by **arundhati** at Feb. 18, 2016

2004 | ISBN: 0521547873, 052183919X | PDF | 352 pages | 6 MB

Posted by **Rare-1** at Dec. 21, 2015

WEBRip | MP4/AVC, ~527 kb/s | 1280 x 720 | English: AAC, 59.4 kb/s (2 ch), 48.0 KHz | 641 MB

Genre: Business / Data & Analytics | Language: English | +Project Files

Learn how to work with time series and all sorts of time related data in R - Forecasting, Time Series Analysis and more

Posted by **ChrisRedfield** at Aug. 3, 2013

Published: 2004-08-04 | ISBN: 0521547873, 052183919X | PDF | 352 pages | 7 MB

Posted by **arundhati** at June 1, 2013

2013 | ISBN-10: 3642334350 | 331 pages | PDF | 3,9 MB

Posted by **spiderman** at Nov. 27, 2006

World Scientific Publishing Company | ISBN 981256117X | April 2005 | PDF | 145 Pages | 13,45 Mb

Nonlinear time series methods have developed rapidly over a quarter of a century and have reached an advanced state of maturity during the last decade. Implementations of these methods for experimental data are now widely accepted and fairly routine, however genuinely useful applications remain rare. The aim of this book is to focus on the practice of applying these methods to solve real problems. It is my hope that the methods presented here are sufficiently accessible, and the examples sufficiently detailed, that practitioners in other areas may use this work to begin considering further applications of nonlinear time series analysis in their own disciplines.

This volume is therefore intended to be accessible to a fairly broad audience:

both specialists in nonlinear time series analysis (for whom many of these techniques may be new); and, scientists in other fields (who may be looking to apply these methods within their speciality). For the experimental scientist looking to use these methods, MATLAB implementation of the underlying algorithms accompany this book.