MATLAB Implementation of the GP Algorithm
GP Algorithm: Simply import one-dimensional time series data for computation. This self-developed implementation has proven highly effective!
Determining Time Delay for Chaotic Time Series Reconstruction Using Autocorrelation Method
This program implements the autocorrelation method to calculate the optimal time delay parameter for reconstructing chaotic time series from observed data.
Prediction of Chaotic Time Series
MATLAB program implementation for chaotic time series prediction with algorithm enhancements
MATLAB Code Implementation for Power Spectrum Analysis with Significance Testing
MATLAB power spectrum analysis with significance testing capabilities, featuring a program that performs brief estimation of autocorrelation processes for time series data, including implementation details using built-in functions like pwelch and xcorr.
Chaotic Time Series Prediction
Chaotic time series prediction program featuring multi-step prediction functions and a main forecasting routine with enhanced algorithmic capabilities.
Time Series Prediction Using Empirical Mode Decomposition and Least Squares Support Vector Machines
A functional MATLAB program that performs empirical mode decomposition on time series data and implements predictive modeling using least squares support vector machines (LS-SVM)
LZ Complexity Reflects the Pattern Growth Rate in Time Series Analysis
LZ complexity measures the rate at which new patterns emerge as a time series length increases. For computational implementation on digital systems, continuous signals must first be discretized - a requirement satisfied by preprocessed heart sound signals. This enables effective heart sound signal separation through algorithmic processing.
Detrending Algorithm for Time Series Analysis (DFA Implementation)
This program implements Detrended Fluctuation Analysis (DFA), a powerful algorithm for removing trends and periodic components from time series data to extract stochastic behavior patterns.
MATLAB Code for Time Series Prediction Using BP Neural Network
This MATLAB code was developed during mathematical modeling for time series prediction using Backpropagation Neural Networks. The implementation is fully functional and includes MATLAB-formatted data for easy validation. The code features detailed comments explaining key algorithm components, making it particularly suitable for beginners learning neural network implementations.
Hurst Parameter for Measuring Time Series Correlation
The Hurst parameter is used to measure the correlation degree of time series data. This resource provides multiple calculation methods including AVB, R/S, VT, PG, and Wavelet approaches with corresponding code implementation considerations.