Handbook of time series analysis : recent theoretical developments and applications
Handbook of time series analysis : recent theoretical developments and applications
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发表时间：
2006
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通讯作者：
B. Schelter;M. Winterhalder;J. Timmer
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作者：
B. Schelter;M. Winterhalder;J. Timmer
前言。1时间序列分析手册：介绍和概述（Bjorn Schelter， Matthias Winterhalder和Jens Timmer）。2时间序列数据的非线性分析（Henry D. I. Abarbanel, Ulrich Parlitz）。2.1简介。2.2展开数据：嵌入定理实践。2.3我们在哪里？2.4李雅普诺夫指数：预测、分类和混沌。2.5预测。2.6建模。2.7结论。参考文献3时间序列预测的局部和聚类加权建模（David Engster和Ulrich Parlitz）。3.1简介。3.2 LocalModeling。3.3聚类加权建模。3.4示例。3.5结论参考文献4重构状态空间中的确定性和概率预测（Holger Kantz和Eckehard Olbrich）。4.1简介。4.2确定性与嵌入。4.3随机过程。4.4事件与分类误差。4.5结论。参考文献5处理生物信号中的随机性（Patrick Celka, Rolf Vetter， Elly Gysels和Trevor J. Hine）。5.2生物系统如何应对或利用随机性？5.2.1生物学中的不确定性原理。5.3科学家和工程师如何应对随机性和噪声？5.4应对方法的选择。5.5应用。5.6结论。参考文献。6鲁棒细节保持信号提取（乌苏拉收集，罗兰弗里德，和薇薇安拉尼乌斯）。6.1介绍。6.2基于局部常数拟合的滤波器。6.3基于局部线性拟合的滤波器。6.4更好地保存移位的修改。6.5结论。参考文献。7耦合振荡器方法在二元数据分析（Michael Rosenblum, Laura Cimponeriu，和Arkady Pikovsky）。7.1双变量数据分析：基于模型与非基于模型的方法。7.3数据耦合的表征。7.4结论与讨论。参考文献8混沌时间序列的非线性动力学模型：方法和应用（Dmitry A. Smirnov和Boris P. Bezruchko）。8.1简介。8.2建模过程方案。8.3“白盒”问题。8.4“灰盒”问题。8.5“黑匣子”问题。8.6经验模型的应用。8.7结论。参考文献。9非平稳脑信号的数据驱动分析（Mario Chavez, Claude Adam， Stefano Boccaletti和Jacques Martinerie）。9.1简介。9.2本征时间尺度分解。9.3强迫系统的本征时间尺度。9.4耦合系统的本征时间尺度。9.5癫痫信号的本征时间尺度。9.6 SEEG数据的时间尺度同步。9.7结论。参考文献10复杂系统中的同步分析和递归（Maria Carmen Romano, Marco Thiel, Jurgen Kurths, Martin Rolfs， Ralf Engbert和Reinhold Kliegl）。10.1简介。10.2通过递归的方式进行相位同步。10.3广义同步和递归。10.4向同步的过渡。10.5用于PS测试的双胞胎替代物。10.6在注视眼动中的应用。10.7结论。参考文献。11噪声和非线性存在下的耦合检测（Theoden I. Neto）。、托马斯·l·卡罗尔、路易斯·m·佩科拉和史蒂文·j·施。)。11.1简介。11.2检测耦合的方法。11.3线性和非线性系统。11.4不耦合系统。11.5弱耦合系统。11.6结论。11.7讨论。参考文献。12多元时间序列的线性模型（Manfred Deistler）。12.1简介。12.2平稳过程与线性系统。12.3多变量状态空间与ARMA(X)模型。12.4时间序列的因子模型。12.5总结与展望。参考文献13生物监测的时空建模（David S. Stoffer and Myron J. Katzo）。13.1简介。13.2背景。13.3状态空间模型。13.4空间约束模型。13.5数据分析。13.6讨论。参考文献。14多元时间序列中动态关系的图形化建模（Michael Eichler）。14.1引言。14.2多元时间序列中的格兰杰因果关系。14.3格兰杰因果关系的图形表示。14.4路径图的马尔可夫解释。14.5统计推断。14.6应用。14.7结论。参考文献。15参数模型的多变量信号分析（Katarzyna J. Blinowska and Maciej Kaminski）。15.1简介。15.2参数化建模。15.3线性模型。15.4模型估计。15.5交叉测量。15.6因果估计。15.7动态过程建模。15.8模拟。15.9实验数据的多变量分析。15.10讨论。15.11致谢。参考文献。16时间序列之间影响的计算机密集测试（Luiz A. Baccala, Daniel Y. Takahashi和Koichi Sameshima）。16.1简介。16.2重采样的基本概念。16.3时间序列重采样。16.4数值例子与应用。16.5讨论。16.6结论。17格兰杰因果关系：神经科学的基础理论与应用（丁明周，陈永红，Steven L. Bressler）。17.1简介。17.2二元时间序列和两两格兰杰因果关系。17.3三元时间序列和条件格兰杰因果关系。17.4自回归模型的估计。17.5数值例子。17.6感觉运动皮层β振荡网络的分析。17.7总结。参考文献。18空间流形的格兰杰因果关系：在神经成像中的应用（Pedro A. Valdes-Sosa, Jose Miguel Bornot-Sanchez, Mayrim Vega-Hernandez, Lester Melie-Garcia， Agustin Lage-Castellanos和Erick Canales-Rodriguez）。18.1绪论。18.2连续空间多元自回归模型及其离散化。18.3空间格兰杰因果关系检验。18.4 sMAR模型的降维方法。18.5惩罚sMAR。18.6 malgorithm估计。18.7模拟数据的评价。18.8真实数据的影响场。18.9可能的扩展和结论。引用。索引。
Preface. List of Contributors. 1 Handbook of Time Series Analysis: Introduction and Overview (Bjorn Schelter, Matthias Winterhalder, and Jens Timmer). 2 Nonlinear Analysis of Time Series Data (Henry D. I. Abarbanel and Ulrich Parlitz). 2.1 Introduction. 2.2 Unfolding the Data: Embedding Theoremin Practice. 2.3 Where are We? 2.4 Lyapunov Exponents: Prediction, Classi.cation, and Chaos. 2.5 Predicting. 2.6 Modeling. 2.7 Conclusion. References. 3 Local and Cluster Weighted Modeling for Time Series Prediction (David Engster and Ulrich Parlitz). 3.1 Introduction. 3.2 LocalModeling. 3.3 Cluster Weighted Modeling. 3.4 Examples. 3.5 Conclusion. References. 4 Deterministic and Probabilistic Forecasting in Reconstructed State Spaces (Holger Kantz and Eckehard Olbrich). 4.1 Introduction. 4.2 Determinism and Embedding 4.3 Stochastic Processes. 4.4 Events and Classification Error. 4.5 Conclusions. References. 5 Dealing with Randomness in Biosignals (Patrick Celka, Rolf Vetter, Elly Gysels, and Trevor J. Hine). 5.1 Introduction. 5.2 How Do Biological Systems Cope with or Use Randomness? 5.2.1 Uncertainty Principle in Biology. 5.3 How Do Scientists and Engineers Cope with Randomness and Noise? 5.4 A Selection of Coping Approaches. 5.5 Applications. 5.6 Conclusions. References. 6 Robust Detail-Preserving Signal Extraction (Ursula Gather, Roland Fried, and Vivian Lanius). 6.1 Introduction. 6.2 Filters Based on Local Constant Fits. 6.3 Filters Based on Local Linear Fits. 6.4 Modi.cations for Better Preservation of Shifts. 6.5 Conclusions. References. 7 Coupled Oscillators Approach in Analysis of Bivariate Data (Michael Rosenblum, Laura Cimponeriu, and Arkady Pikovsky). 7.1 Bivariate Data Analysis: Model-Based Versus Nonmodel-Based Approach. 7.2 Reconstruction of PhaseDynamics fromData. 7.3 Characterization of Coupling from Data. 7.4 Conclusions and Discussion. References. 8 Nonlinear Dynamical Models from Chaotic Time Series: Methods and Applications (Dmitry A. Smirnov and Boris P. Bezruchko). 8.1 Introduction. 8.2 Scheme of theModeling Process. 8.3 "White Box" Problems. 8.4 "Gray Box" Problems. 8.5 "Black Box" Problems. 8.6 Applications of Empirical Models. 8.7 Conclusions. References. 9 Data-Driven Analysis of Nonstationary Brain Signals (Mario Chavez, Claude Adam, Stefano Boccaletti and Jacques Martinerie). 9.1 Introduction. 9.2 Intrinsic Time-Scale Decomposition. 9.3 Intrinsic Time Scales of Forced Systems. 9.4 Intrinsic Time Scales ofCoupled Systems. 9.5 Intrinsic Time Scales of Epileptic Signals. 9.6 Time-Scale Synchronization of SEEG Data. 9.7 Conclusions. References. 10 Synchronization Analysis and Recurrence in Complex Systems (Maria Carmen Romano, Marco Thiel, Jurgen Kurths, Martin Rolfs, Ralf Engbert, and Reinhold Kliegl). 10.1 Introduction. 10.2 Phase Synchronization by Means of Recurrences. 10.3 Generalized Synchronization and Recurrence. 10.4 Transitions to Synchronization. 10.5 Twin Surrogates to Test for PS. 10.6 Application to Fixational Eye Movements. 10.7 Conclusions. References. 11 Detecting Coupling in the Presence of Noise and Nonlinearity (Theoden I. Neto., Thomas L. Carroll, Louis M. Pecora, and Steven J. Schi. ). 11.1 Introduction. 11.2 Methods of Detecting Coupling. 11.3 Linear and Nonlinear Systems. 11.4 Uncoupled Systems. 11.5 Weakly Coupled Systems. 11.6 Conclusions. 11.7 Discussion. References. 12 Linear Models for Mutivariate Time Series (Manfred Deistler). 12.1 Introduction. 12.2 Stationary Processes and Linear Systems. 12.3 Multivariable State Space and ARMA(X) Models. 12.4 Factor Models for Time Series. 12.5 Summary and Outlook. References. 13 Spatio-Temporal Modeling for Biosurveillance (David S. Stoffer and Myron J. Katzo.). 13.1 Introduction. 13.2 Background. 13.3 The State Space Model. 13.4 Spatially Constrained Models. 13.5 Data Analysis. 13.6 Discussion. References. 14 Graphical Modeling of Dynamic Relationships in Multivariate Time Series (Michael Eichler). 14.1 Introduction. 14.2 Granger Causality in Multivariate Time Series. 14.3 Graphical Representations of Granger Causality. 14.4 Markov Interpretation of Path Diagrams. 14.5 Statistical Inference. 14.6 Applications. 14.7 Conclusion. References. 15 Multivariate Signal Analysis by Parametric Models (Katarzyna J. Blinowska and Maciej Kaminski). 15.1 Introduction. 15.2 Parametric Modeling. 15.3 Linear Models. 15.4 Model Estimation. 15.5 Cross Measures. 15.6 Causal Estimators. 15.7 Modeling of Dynamic Processes 15.8 Simulations. 15.9 Multivariate Analysis of Experimental Data. 15.10 Discussion. 15.11 Acknowledgements. References. 16 Computer Intensive Testing for the Influence Between Time Series (Luiz A. Baccala, Daniel Y. Takahashi, and Koichi Sameshima). 16.1 Introduction. 16.2 Basic Resampling Concepts. 16.3 Time Series Resampling. 16.4 Numerical Examples and Applications. 16.5 Discussion. 16.6 Conclusions. References. 17 Granger Causality: Basic Theory and Application to Neuroscience (Mingzhou Ding, Yonghong Chen, and Steven L. Bressler). 17.1 Introduction. 17.2 Bivariate Time Series and Pairwise Granger Causality. 17.3 TrivariateTime Series and Conditional Granger Causality. 17.4 Estimation of Autoregressive Models. 17.5 Numerical Examples. 17.6 Analysis of a Beta Oscillation Network in Sensorimotor Cortex. 17.7 Summary. References. 18 Granger Causality on Spatial Manifolds: Applications to Neuroimaging (Pedro A. Valdes-Sosa, Jose Miguel Bornot-Sanchez, Mayrim Vega-Hernandez, Lester Melie-Garcia, Agustin Lage-Castellanos, and Erick Canales-Rodriguez). 18.1 Introduction. 18.2 The Continuous Spatial Multivariate Autoregressive Model and its Discretization. 18.3 Testing for Spatial Granger Causality. 18.4 Dimension Reduction Approaches to sMAR Models. 18.5 Penalized sMAR. 18.6 Estimation via the MMAlgorithm. 18.7 Evaluation of Simulated Data. 18.8 Influence Fields for Real Data. 18.9 Possible Extensions and Conclusions. References. Index.