Modelling and Forecasting Financial Data : Techniques of Nonlinear Dynamics

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.