非线性时间序列分析（第2版 英文版）

非线性时间序列分析（第2版 英文版）
出版时间：2015年版
内容简介
《非线性时间序列分析（第2版）》旨在以动力系统理论为基础，阐述时间序列分析的现代方法。这部修订版，增加了一些新的章节，对原版进行了大量的修订和扩充。从潜在的理论出发，到实际应用话题，并用众多领域收集来的大量经验数据解释这些实用话题。本书对研究时间变量信号的各个领域包括地球、生命科学科学家和工程人员都十分有用。
　　目次：基本话题：导论；线性工具和一般考虑；相空间方法；确定论和可预测性；不稳定性：Lyapunov指数；自相似性：当决定论是弱的时候非线性方法的应用；非线性线性精选；高等话题：高等浸入式方法；混沌数据和噪音；更多有关不变量；模型和预测；非平稳信号；耦合和非线性系统综合；混沌控制。A:TISEAN程序应用；B：实验数据集合描述。
　　读者对象：数学、生命科学、经济等众多实践应用领域的科研人员。
目录
Preface to the first edition
Preface to the second edition
Acknowledgements
Ⅰ Basictopics
1 Introduction： why nonlinear methods？
2 Linear tools and general considerations
2.1 Stationarity and sampling
2.2 Testing for stationarity
2.3 Linear correlations and the power spectrum
2.3.1 Stationarity and the low—frequency component in the power spectrum
2.4 Linear filters
2.5 Linearpredictions
3 Phase space methods
3.1 Determinism： uniqueness in phase space
3.2 Delayreconstruction
3.3 Finding a good embedding
3.3.1 False neighbours
3.3.2 The time lag
3.4 Visual inspection of data
3.5 Poincare surface of section
3.6 Recurrenceplots
4 Determinism and predictability
4.1 Sources of predictability
4.2 Simple nonlinear prediction algorithm
4.3 Verification ofsuccessful prediction
4.4 Cross—prediction errors： probing stationarity
4.5 Simple nonlinear noise reduction
5 Instability： Lyapunov exponents
5.1 Sensitive dependence on initial conditions
5.2 Exponentialdivergence
5.3 Measuring the maximalexponent from data
6 Self—similarity：dimensions
6.1 Attractor geometry and fractals
6.2 Correlationdimension
6.3 Correlation sum from a time series
6.4 Interpretation and pitfalls
6.5 Temporalcorrelations， non—stationarity， and space time separation plots
6.6 Practicalconsiderations
6.7 A useful application： determination of the noise level using the correlation integral
6.8 Multi—scale or self—similar signals
6.8.1 Scalinglaws
6.8.2 Detrended fluctuation analysis
7 Using nonlinear methods when determinismis weak
7.1 Testing for nonlinearity with surrogate data
7.1.1 The null hypothesis
7.1.2 How to make surrogate data sets
7.1.3 Which statistics to use
7.1.4 What can go wrong
7.1.5 What we havelearned
7.2 Nonlinear statistics for system discrimination
7.3 Extracting qualitative information from a time series
8 Selected nonlinear phenomena
8.1 Robustness and limit cycles
8.2 Coexistence of attractors
8.3 Transients
8.4 Intermittency
8.5 Structural stabilitY
8.6 Bifurcations
8.7 Quasi—periodicity
Ⅱ Advancedtopics
9 Advanced embedding methods
9.1 Embedding theorems
9.1.1 Whitney's embedding theorem
9.1.2 Takens's delay embedding theorem
9.2 The time lag
9.3 Filtered delay embeddings
9.3.1 Derivative coordinates
9.3.2 Principal component analysis
9.4 Fluctuating time intervals
9.5 Multichannel measurements
9.5.1 Equivalent variables at different positions
9.5.2 Variables with different physical meanings
9.5.3 Distributed systems
9.6 Embedding of interspike intervals
9.7 High dimensional chaos and the limitations of the time delay embedding
9.8 Embedding for systems with time delayed feedback
10 Chaotic data and noise
10.1 Measurement noise and dynamical noise
10.2 Effects of noise
10.3 Nonlinear noise reduction
10.3.1 Noise reduction by gradient descent
10.3.2 Local projective noise reduction
10.3.3 Implementation oflocally projective noise reduction
10.3.4 How much noise.is taken out？
10.3.5 Consistencytests
10.4 An application： foetal ECG extraction
11 More aboutinvariant quantities
11.1 Ergodicity and strange attractors
11.2 Lyapunov exponents Ⅱ
11.2.1 The spectrum of Lyapunov exponents and invariant manifolds
11.2.2 Flows versus maps
11.2.3 Tangent space method
11.2.4 Spuriousexponents
11.2.5 Almost two dimensional flows
11.3 Dimensions Ⅱ
11.3.1 Generalised dimensions， multi—fractals
11.3.2 Information dimension from a time series
11.4 Entropies
11.4.1 Chaos and the flow ofinformation
11.4.2 Entropies of a static distribution
11.4.3 The Kolmogorov—Sinai entropy
11.4.4 The e—entropy per unit time
11.4.5 Entropies from time series data
11.5 How things are related
11.5.1 Pesin'sidentity
11.5.2 Kaplan—Yorkeconjecture
12 Modelling and forecasting
12.1 Linear stochastic models and filters
12.1.1 Linear filters
12.1.2 Nonlinear filters
12.2 Deterministicdynamics
12.3 Local methods in phase space
12.3.1 Almost model free methods
12.3.2 Local linear fits
12.4 Global nonlinear models
12.4.1 Polynomials
12.4.2 Radial basis functions
12.4.3 Neuralnetworks
12.4.4 What to do in practice
12.5 Improved cost functions
12.5.1 Overfitting and modelcosts
12.5.2 The errors—in—variables problem
12.5.3 Modelling versus prediction
12.6 Model verification
12.7 Nonlinear stochastic processes from data
12.7.1 Fokker—Planck equations from data
12.7.2 Markov chains in embedding space
12.7.3 No embedding theorem for Markov chains
12.7.4 Predictions for Markov chain data
12.7.5 Modelling Markov chain data
12.7.6 Choosing embedding parameters for Markov chains
12.7.7 Application： prediction of surface wind velocities
12.8 Predicting prediction errors
12.8.1 Predictabilitymap
12.8.2 Individual error prediction
12.9 Multi—step predictions versus iterated one—step predictions
13 Non—stationary signals
13.1 Detecting non—stationarity
13.1.1 Making non—stationary data stationary
13.2 Over—embedding
13.2.1 Deterministic systems with parameter drift
13.2.2 Markov chain with parameter drift
13.2.3 Data analysis in over—embedding spaces
13.2.4 Application： noise reduction for human voice
13.3 Parameter spaces from data
14 Coupling and synchronisation of nonlinear systems
14.1 Measures for interdependence
14.2 Transfer entropy
14.3 Synchronisation
15 Chaos control
15.1 Unstable periodic orbits and their invariant manifolds
15.1.1 Locating periodic orbits
15.1.2 Stable／unstable manifolds from data
15.2 OGY—control and derivates
15.3 Variants of OGY—control
15.4 Delayed feedback
15.5 Tracking
15.6 Relatedaspects
A Using the TISEAN programs
A.1 Information relevant to most of the routines
A.1.1 Efficient neighbour searching
A.1.2 Re—occurring command options
A.2 Second—order statistics and linear models
A.3 Phase space tools
A.4 Prediction and modelling
A.4.1 Locally constant predictor
A.4.2 Locally linear prediction
A.4.3 Global nonlinear models
A.5 Lyapunov exponents
A.6 Dimensions and entropies
A.6.1 The correlation sum
A.6.2 Information dimension， fixed mass algorithm
A.6.3 Entropies
A.7 Surrogate data and test statistics
A.8 Noise reduction
A.9 Finding unstable periodic orbits
A.10 Multivariate data
B Description of the experimental data sets
B.1 Lorenz—like chaos in an NH3 laser
B.2 Chaos in a periodically modulated NMR laser
B.3 Vibrating string
B.4 Taylor—Couette flow
B.5 Multichannel physiological data
B.6 Heart rate during atrial fibrillation
B.7 Human electrocardiogram （ECG）
B.8 Phonation data
B.9 Postural control data
B.10 Autonomous CO2 laser with feedback
B.11 Nonlinear electric resonance Circuit
B.12 Frequency doubling solid state laser
B.13 Surface wind velocities
References
Index