Wavelet Methods For Time Series Analysis
Par : ,Formats :
Définitivement indisponible
Cet article ne peut plus être commandé sur notre site (ouvrage épuisé ou plus commercialisé). Il se peut néanmoins que l'éditeur imprime une nouvelle édition de cet ouvrage à l'avenir. Nous vous invitons donc à revenir périodiquement sur notre site.
- Nombre de pages594
- PrésentationRelié
- Poids1.255 kg
- Dimensions18,5 cm × 26,0 cm × 4,0 cm
- ISBN0-521-64068-7
- EAN9780521640688
- Date de parution06/10/2000
- Collectioncambridge series in statistica
- ÉditeurCambridge University Press
Résumé
Data in the form of time series are routinely collected in science, engineering, and other areas such as finance and economics. This is an introduction to wavelet analysis 'from the ground level and up', and to wavelet-based statistical analysis of time series. It focuses on practical discrete time techniques, with detailed descriptions of the theory and algorithms needed to understand and implement the discrete wavelet transforms. Numerous examples illustrate the techniques on actual time series. The many embedded exercises - with full solutions provided in the Appendix - allow use of the book for self-guided study; additional exercises can be used in a classroom setting. A Web site gives access to the time series and wavelets used in the book, as well as information on obtaining software in S-Plus and other languages. This book will be welcomed by all students and researchers wishing to use wavelet methods to analyse time series.
Data in the form of time series are routinely collected in science, engineering, and other areas such as finance and economics. This is an introduction to wavelet analysis 'from the ground level and up', and to wavelet-based statistical analysis of time series. It focuses on practical discrete time techniques, with detailed descriptions of the theory and algorithms needed to understand and implement the discrete wavelet transforms. Numerous examples illustrate the techniques on actual time series. The many embedded exercises - with full solutions provided in the Appendix - allow use of the book for self-guided study; additional exercises can be used in a classroom setting. A Web site gives access to the time series and wavelets used in the book, as well as information on obtaining software in S-Plus and other languages. This book will be welcomed by all students and researchers wishing to use wavelet methods to analyse time series.