Stock Market Nonlinear Analysis and Prediction Toolbox

MATLAB 39K 223 views 0 downloads 1 credits
Tags:

Resource Overview

Stock Market Nonlinear Analysis and Prediction Toolbox integrates the original nonlinear time series analysis toolbox programs, featuring multiple complexity analysis methods (such as Higuchi's method, box-counting method), phase space reconstruction techniques (Cao's method, GP algorithm, mutual information method), maximum Lyapunov exponent determination (Wolf's method, small data sets method) and prediction procedures (Lyapunov exponent method, one-step multi-step prediction, etc.). The toolbox demonstrates high execution efficiency and practical usability, with optimized algorithms for real-world financial data processing.

Detailed Documentation

This article introduces a nonlinear analysis and prediction toolbox specifically designed for stock market analysis. The toolbox integrates the original time series analysis programs and incorporates multiple sophisticated algorithms including complexity analysis methods (Higuchi's method for fractal dimension estimation, box-counting method for dimensionality calculation), phase space reconstruction techniques (Cao's method for embedding dimension determination, GP algorithm for optimal embedding parameters, mutual information method for time delay selection), maximum Lyapunov exponent calculation (Wolf's method for chaotic system identification, small data sets method for efficient computation) and prediction routines (Lyapunov exponent-based forecasting, multi-step ahead prediction algorithms). The implementation features optimized code architecture that ensures high computational efficiency, with thorough validation confirming practical applicability for financial analysts conducting advanced data analysis and market prediction tasks.

You May Also Like