Handbook of Time Series Analysis
Edited by
Björn Schelter,
Mathias Winterhalder,
and Jens Timmer

This handbook comprises a wide range of current topics in the field of time series
analysis. The editors are well-known for both their theoretical work on time series
analysis techniques and their applications. Therefore, the editors attached great
importance to both theoretical work and applications. Especially, the interplay of
theory and practice is included in this Handbook of Time Series Analysis. The
editors brought together contributions of worldwide accepted experts of different
branches, e.g. from Physics, Mathematics, Biology, Medicine, Neuroscience,
and Engineering. With respect to the theory this Handbook covers a broad variety
of presently used methodologies in different disciplines, ranging from linear
stochastic systems to Nonlinear Dynamics, from univariate to multivariate time
series analysis.

Contents

1 Handbook of Time Series Analysis: Introduction and Overview

2 Nonlinear Analysis of Time Series Data

3 Local and Cluster Weighted Modeling for Time Series Prediction

4 Deterministic and Probabilistic Forecasting in Reconstructed State Spaces

5 Dealing with Randomness in Biosignals

6 Robust Detail-Preserving Signal Extraction

7 Coupled Oscillators Approach in Analysis of Bivariate Data

8 Nonlinear Dynamical Models from Chaotic Time Series

9 Data-Driven Analysis of Nonstationary Brain Signals

10 Synchronization Analysis and Recurrence in Complex Systems

11 Detecting Coupling in the Presence of Noise and Nonlinearity

12 Linear Models for Mutivariate Time Series

13 Spatio-Temporal Modeling for Biosurveillance

14 Graphical Modeling of Dynamic Relationships in Multivariate Time Series

15 Multivariate Signal Analysis by Parametric Models

16 Computer Intensive Testing for the Influence Between Time Series

17 Granger Causality: Basic Theory and Application to Neuroscience

18 Granger Causality on Spatial Manifolds: Applications to Neuroimaging