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

The Editors are members of the research group "Data Analysis and Modeling of Dynamic Processes in the Life Sciences" at the Center for Data Analysis and Modeling of the University of Freiburg, Germany. In interdisciplinary projects, they develop, investigate and apply mathematical methods to elucidate properties of complex systems in the field of neurology based on multivariate time series.

This handbook provides an up-to-date survey of current research topics and applications of time series analysis methods written by leading experts in their fields. It covers recent developments in linear and non-linear, as well as stochastic and deterministic analysis methods and their cross-fertilization between Mathematics, Physics, and Engineering. Each chapter comprises both methodological aspects and applications to time series from real world complex systems, such as the human brain or Earth's climate. Covering an exceptionally broad spectrum of topics, beginners, experts and practitioners in time series analysis who seek to understand the latest developments will profit from this handbook.<br> <br> Foto von links nach rechts: M. Winterhalder, J. Timmer, B. Schelter<br> <br> The Editors are working in the research group "Data Analysis and Modeling of Dynamic Processes in the Life Sciences" at the Center for Data Analysis and Modeling of the University of Freiburg, Germany. In interdisciplinary projects, they develop, investigate and apply mathematical methods to elucidate properties of complex systems in the field of neurology based on multivariate time series.

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