• I Embedding Theory: Time-Delay Phase Space Reconstruction and Detection of Nonlinear Dynamics.- 1 Embedding Theory: Introduction and Applications to Time Series Analysis.- 2 Determining Minimum Embedding Dimension.- 3 Mutual Information and Relevant Variables for Predictions.- II Methods of Nonlinear Modelling and Forecasting.- 4 State Space Local Linear Prediction.- 5 Local Polynomial Prediction and Volatility Estimation in Financial Time Series.- 6 Kalman Filtering of Time Series Data.- 7 Radial Basis Functions Networks.- 8 Nonlinear Prediction of Time Series Using Wavelet Network Method.- III Modelling and Predicting Multivariate and Input-Output Time Series.- 9 Nonlinear Modelling and Prediction of Multivariate Financial Time Series.- 10 Analysis of Economic Time Series Using NARMAX Polynomial Models.- 11 Modeling dynamical systems by Error Correction Neural Networks.- IV Problems in Modelling and Prediction.- 12 Surrogate Data Test on Time Series.- 13 Validation of Selected Global Models.- 14 Testing Stationarity in Time Series.- 15 Analysis of Economic Delayed-Feedback Dynamics.- 16 Global Modeling and Differential Embedding.- 17 Estimation of Rules Underlying Fluctuating Data.- 18 Nonlinear Noise Reduction.- 19 Optimal Model Size.- 20 Influence of Measured Time Series in the Reconstruction of Nonlinear Multivariable Dynamics.- V Applications in Economics and Finance.- 21 Nonlinear Forecasting of Noisy Financial Data.- 22 Canonical Variate Analysis and its Applications to Financial Data.

"This book is truly a multidisciplinary effort, with contributors including economists, electrical engineers, physicists, mathematicians, and statisticians (myself and Jianming Ye). Although there are many books on nonlinear dynamic techniques, Modelling and Forecasting Financial Data is distinguished by its concerted efforts on practical relevance in financial and economic applications." (Z.-Q. John Lu, National Institute of Standards and Technology in Technometrics, 46:1 (February 2004)