Modelling and Forecasting Financial Data

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.