AR Model Source Code
Source code implementation for the AR (Augmented Reality) model system.
Time Series AR Model Establishment and Autocorrelation Coefficient Extraction for Classification
Time Series AR Model Development and Autocorrelation Feature Extraction for Classification with Algorithm Implementation
MATLAB Program for AR Model Order Selection and Parameter Estimation
This MATLAB program implements autoregressive (AR) model order determination and parameter estimation for time series analysis and forecasting, featuring comprehensive implementation of key algorithms including Yule-Walker equations and information criteria (AIC/BIC) for optimal model selection.
Modern Spectral Estimation Using AR Models
Implementation and comparative analysis of modern spectral estimation methods including autocorrelation method, covariance method, and Burg method based on AR models, with performance evaluation and comparison against classical spectral estimation techniques.
Classic Time Series Analysis Methods with Implementation Examples
This article introduces fundamental time series methodologies including Autoregressive (AR), Autoregressive Moving Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA) models for stationary forecasting problems, accompanied by practical code implementations for immediate application.
MATLAB for Neural Electrical Signal Analysis
Feature extraction and classification of time-series EMG signals using AR modeling techniques with MATLAB implementation
AIC Model Order Determination Criterion for AR Models with Code Implementation Insights
This content explains the Akaike Information Criterion (AIC) for determining optimal model order in AR models, including practical implementation approaches and algorithm considerations for time series analysis.
Parameter Estimation Using AR Model in Modern Power Spectrum Estimation
AR model-based parameter estimation in modern power spectrum analysis provides superior resolution compared to classical spectral estimation methods, offering enhanced frequency component detection through autoregressive coefficient optimization.
Implementation of Levinson-Durbin Algorithm Using MATLAB
Function Description: MATLAB implementation of the Levinson-Durbin algorithm for autoregressive (AR) model parameter estimation. The algorithm efficiently computes AR coefficients through recursive solution of the Yule-Walker equations using backward prediction errors and reflection coefficients. Function Name: Levinson_Durbin_Algo. Input Parameters: (1) R: autocorrelation matrix or its estimate, (2) P: order of the AR model. Output Parameters: (1) A: row vector of length P+1 containing AR model parameters, (2) E: noise power (prediction error variance). Calling Functions: none. Called By: L_D_sim.m. Author: mingcheng. Creation Date: 2009-11-13. Modification Date: 2009-11-13. Version: V1.0.
AR Model Coefficient Prediction Using Kalman Algorithm
This implementation demonstrates AR model coefficient prediction enhanced by Kalman filtering algorithm, featuring real-time coefficient updates that significantly improve prediction accuracy through recursive state estimation and measurement updates.