Chaos Time Series Analysis and Prediction Toolkit Version 2.0

The Chaos Time Series Analysis and Prediction Toolkit Version 2.0 provides a comprehensive suite of algorithms for handling nonlinear dynamical systems. The toolkit implements sophisticated methods including phase space reconstruction using Takens' embedding theorem, calculation of Lyapunov exponents for chaos detection, and correlation dimension analysis for fractal characterization. The prediction module employs advanced techniques such as local linear prediction, neural networks, and support vector machines (SVM) for accurate multi-step forecasting. A key enhancement in Version 2.0 is the optimized handling of large-scale datasets through efficient memory management and parallel computing capabilities. The toolkit includes specialized functions for time delay embedding with automatic parameter selection using mutual information and false nearest neighbors methods. For financial data analysis, it incorporates volatility modeling and regime-switching detection algorithms, while meteorological applications benefit from specialized seasonal decomposition and anomaly detection routines. The visualization subsystem offers interactive 2D/3D phase space plots, recurrence plots, bifurcation diagrams, and prediction error analysis charts. Developers can access the core algorithms through a well-documented API supporting MATLAB, Python, and C++ interfaces. The toolkit includes example scripts demonstrating applications ranging from stock market prediction to climate modeling, featuring complete workflow implementations from data preprocessing to model validation. With its robust implementation of chaos theory principles and machine learning integration, Version 2.0 enables researchers and analysts to perform sophisticated time series analysis that was previously limited to specialized research environments. The object-oriented architecture allows easy extension of existing algorithms and integration with custom prediction models.