Time Series Chaos Analysis for Graduation Research
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Resource Overview
Graduation project focusing on time series chaos analysis, implementing phase space reconstruction, Lyapunov exponent calculation, wavelet denoising techniques with Python/MATLAB code examples
Detailed Documentation
In my graduation thesis, I will conduct time series chaos analysis. This analytical approach encompasses several key techniques including phase space reconstruction using Takens' embedding theorem, calculation of Lyapunov exponents to quantify system sensitivity, and wavelet denoising for signal preprocessing. The implementation involves Python libraries such as NumPy for numerical computations and SciPy for signal processing, with specific functions like scipy.signal.cwt for continuous wavelet transforms. Through the application of these techniques, I will deeply investigate the dynamic characteristics of time series data, revealing underlying patterns and trends. This comprehensive analysis will provide me with deeper insights and offer more reliable support for my research findings, particularly through quantitative measures of chaotic behavior using algorithms like the Wolf method for Lyapunov exponent estimation.
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