1. Auflage - September 2006
205,- Euro
2006. XVIII, 496 Seiten, Hardcover
162 Abb. (3 Farbabb.), 19 Tab. 
- Handbuch/Nachschlagewerk -
ISBN-10: 3-527-40623-9
ISBN-13: 978-3-527-40623-4 - Wiley-VCH, Berlin

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Probekapitel



Kurzbeschreibung
Anerkannte Experten auf dem Gebiet bieten einen aktuellen Überblick über neue Entwicklungen und Anwendungen der Zeitreihenanalyse.

Aus dem Inhalt
1 Overview: Handbook of time series analysis
(B. Schelter, M. Winterhalder, and J. Timmer)
2 Nonlinear Analysis of Time Series Data
(Henry D. I. Abarbanel and Ulrich Parlitz)
3 Local and Cluster Weighted Modeling for Time Series Prediction
(David Engster and Ulrich Parlitz)
4 Deterministic and probabilistic forecasting in reconstructed state spaces
(Holger Kantz and Eckehard Olbrich)
5 Dealing with randomness in biosignals
(Patrick Celka, Rolf Vetter, Elly Gysels, and Trevor Hine)
6 Robust detail-preserving signal extraction
(Ursula Gather, Roland Fried, and Vivian Lanius)
7 Coupled oscillators approach in analysis of bivariate data
(Michael Rosenblum, Laura Cimponeriu, and Arkady Pikovsky)
8 Nonlinear dynamical models from chaotic time series: methods and applications
(D.A. Smirnov and B.P. Bezruchko)
9 Data-driven analysis of nonstationary brain signals
(Mario Chavez, Claude Adam, Stefano Boccaletti, and Jacques Martinerie)
10 Synchronization Analysis and Recurrence in Complex Systems
(M. C. Romano, M. Thiel, J. Kurths, M. Rolfs, R. Engbert, and R. Kliegl)
11 Detecting coupling in the presence of noise and nonlinearity
(Theoden I. Netoff, Thomas L. Carroll, Louis M. Pecora, and Steven J. Schiff)
12 Linear models for mutivariate time series
(Manfred Deistler)
13 Spatio-TemporalModeling for Biosurveillance Using a Spatially Constrained State Space Model
(David S. Stoffer and Myron J. Katzoff)
14 Graphical modeling of dynamic relationships in multivariate time series
(Michael Eichler)
15 Multivariate Signal Analysis by ParametricModels
(K. J. Blinowska and M. Kaminski)
16 Computer Intensive Testing for the Influence between Time Series
(Luiz A. Baccalá, Daniel Y. Takahashi, and Koichi Sameshima)
17 Granger Causality: Basic Theory and Application to Neuroscience
(Mingzhou Ding, Yonghong Chen, and Steven L. Bressler)
18 Granger Causality on Spatial Manifolds: applications to Neuroimaging
(P. A. Valdés-Sosa, J.M. Bornot-Sánchez, M. Vega-Hernández, L. Melie-García, A. Lage-Castellanos, and E. Canales-Rodríguez)