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Synopsis
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 univariate as well as bivariate and multivariate time series analysis techniques ranging from physics' to life sciences' applications. Each chapter comprises both methodological aspects and applications to real world complex systems, such as the human brain or Earth's climate. Covering an exceptionally broad spectrum of topics, beginners, experts and practitioners who seek to understand the latest developments will profit from this handbook.
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From the Back Cover
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.
Foto von links nach rechts: M. Winterhalder, J. Timmer, B. Schelter
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.
About the Author
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.
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- PublisherWiley-VCH
- Publication date2006
- ISBN 10 3527406239
- ISBN 13 9783527406234
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages514
- EditorSchelter Björn, Winterhalder Matthias, Timmer Jens
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Handbook of Time Series Analysis: Recent Theoretical Developments and Applications
Published by
Wiley-VCH, 2006
ISBN 10: 3527406239
ISBN 13: 9783527406234
Used
Hardcover
First Edition
Seller: killarneybooks, Inagh, CLARE, Ireland
Seller rating 5 out of 5 stars
Hardcover. Condition: Good. 1st Edition. Oversized hardcover, xviii + 496 pages. Weight 1070g. Cleanly removed title-page, blank labels inside the front board, a thick external black-marker line on upper outer page edges. Book is clean, with unmarked text and firm binding. Short creases in the upper corner of the front board. Issued without a dust jacket. -- Contents [an Introduction & Conclusion in each chapter]: 1 Introduction & Overview 2 Nonlinear Analysis of Time Series Data [Unfolding the Data: Embedding Theorem in Practice; Where are We?; Lyapunov Exponents: Prediction, Classification, and Chaos; Predicting; Modeling] 3 Local and Cluster Weighted Modeling for Time Series Prediction [Local Modeling; Cluster Weighted Modeling; Examples] 4 Deterministic and Probabilistic Forecasting in Reconstructed State Spaces [Determinism and Embedding; Stochastic Processes; Events and Classification Error] 5 Dealing with Randomness in Biosignals [How Do Biological Systems Cope with or Use Randomness?; How Do Scientists & Engineers Cope with Randomness & Noise?; A Selection of Coping Approaches; Applications] 6 Robust Detail-Preserving Signal Extraction [Filters Based on Local Constant Fits; Filters Based on Local Linear Fits; Modifications for Better Preservation of Shifts] 7 Coupled Oscillators Approach in Analysis of Bivariate Data [Bivariate Data Analysis: Model-Based Versus Nonmodel-Based; Reconstruction of Phase Dynamics from Data; Characterization of Coupling from Data] 8 Nonlinear Dynamical Models from Chaotic Time Series: Methods & Applications [Scheme of the Modeling Process; 'White Box' Problems; 'Gray Box' Problems; 'Black Box' Problems; Applications of Empirical Models] 9 Data-Driven Analysis of Nonstationary Brain Signals [Intrinsic Time-Scale Decomposition; Intrinsic Time Scales of Forced Systems; Intrinsic Time Scales of Coupled Systems; Intrinsic Time Scales of Epileptic Signals; Time-Scale Synchronization of SEEG Data] 10 Synchronization Analysis & Recurrence in Complex Systems [Phase Synchronization by Means of Recurrences; Generalized Synchronization & Recurrence; Transitions to Synchronization; Twin Surrogates to Test for PS; Application to Fixational Eye Movements] 11 Detecting Coupling in the Presence of Noise & Nonlinearity [Methods of Detecting Coupling; Linear & Nonlinear Systems; Uncoupled Systems; Weakly Coupled Systems] 12 Linear Models for Mutivariate Time Series [Stationary Processes & Linear Systems; Multivariable State Space & ARMA(X) Models; Factor Models for Time Series] 13 Spatio-Temporal Modeling for Biosurveillance [Background; State Space Model; Spatially Constrained Models; Data Analysis] 14 Graphical Modeling of Dynamic Relationships in Multivariate Time Series [Granger Causality in Multivariate Time Series; Graphical Representations of Granger Causality; Markov Interpretation of Path Diagrams; Statistical Inference; Applications] 15 Multivariate Signal Analysis by Parametric Models [Parametric Modeling; Linear Models; Model Estimation; Cross Measures; Causal Estimators; Modeling of Dynamic Processes; Simulations; Multivariate Analysis of Experimental Data] 16 Computer Intensive Testing for the Influence Between Time Series [Basic Resampling Concepts; Time Series Resampling; Numerical Examples & Applications; Discussion] 17 Granger Causality: Basic Theory & Application to Neuroscience [Bivariate Time Series & Pairwise Granger Causality; Trivariate Time Series & Conditional Granger Causality; Estimation of Autoregressive Models; Numerical Examples; Analysis of a Beta Oscillation Network in Sensorimotor Cortex] 18 Granger Causality on Spatial Manifolds: Applications to Neuroimaging [Continuous Spatial Multivariate Autoregressive Model & its Discretization; Testing for Spatial Granger Causality; Dimension Reduction Approaches to sMAR Models; Penalized sMAR; Estimation via the MM Algorithm; Evaluation of Simulated Data; Influence Fields for Real Data; Possible Extensions & Conclusions]; Index. Seller Inventory # 009246
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Handbook of Time Series Analysis: Recent Theoretical Developments and Applications
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hardcover. Condition: Befriedigend. 514 Seiten; 9783527406234.4 Gewicht in Gramm: 2. Seller Inventory # 862813
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Handbook of Time Series Analysis: Recent Theoretical Developments and Applications
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