The article explores chaotic patterns in economic time series using phase space reconstruction and Lyapunov exponents. It introduces methods like false neighboring and auto-relativity function to determine time delay and embedding dimension. The researchers also present a way to calculate Lyapunov exponents. Finally, they apply these techniques to analyze the Shanghai Stock Market's logarithm yield for chaotic characteristics.