Algorithm for Piecewise Linear Segmentation
A bottom-up piecewise linear time series segmentation algorithm that recursively merges segments while optimizing linear approximation errors
Source Code for Calculating Time Series Average Period Using FFT Method
Implementation of FFT-based algorithm to compute the average period of time series data. This straightforward yet effective approach provides reliable results and serves as a valuable reference for signal processing applications.
Time Series Forecasting
Time series data refers to a sequence of observations arranged in chronological order, such as annual GDP figures and population statistics. The primary objective of time series modeling is data forecasting, including applications like predicting future sales volumes and stock price movements. This involves key techniques like trend decomposition, seasonal adjustment, and stationarity transformation.
Time Series Prediction Using Support Vector Machine Regression and Phase Space Reconstruction
Implementation of time series forecasting combining Support Vector Machine Regression with Phase Space Reconstruction methodology
MFDCCA: Multifractal Detrended Cross-Correlation Analysis Algorithm Implementation
A fractal methodology program for investigating cross-correlation relationships between two non-stationary time series, featuring numerical implementation of detrending procedures and scaling analysis.
Time Series Prediction Using Wavelet Neural Networks
Implementation of time series forecasting through wavelet neural networks with four MATLAB m-files demonstrating the complete prediction model architecture
Multiple Approaches for Calculating Hurst Exponent in Time Series Analysis
While various methods exist for computing Hurst exponents in time series, wavelet-based approaches offer superior accuracy. This MATLAB implementation specifically utilizes wavelet transforms to calculate Hurst coefficients, providing robust algorithmic solutions for fractal analysis.
Neural Network Time Series Prediction with MATLAB Implementation
Time series prediction using neural networks demonstrated through sunspot data and rotor fault signal analysis, featuring MATLAB programming approaches including data preprocessing, network architecture selection, and prediction accuracy evaluation
Stock Market Nonlinear Analysis and Prediction Toolbox
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.
Comprehensive MATLAB Function Suite for Fault Diagnosis
A self-developed comprehensive MATLAB function collection for fault diagnosis featuring statistical methods, time-domain analysis, time-series techniques, spectral analysis with power spectral density functions, and wavelet analysis methods. Each algorithm includes detailed implementation comments and usage guidelines.