Explore MATLAB source code curated for "自回归" with clean implementations, documentation, and examples.
This MATLAB implementation of autoregressive time series forecasting for short-term electricity load has been successfully deployed in practical engineering applications. The algorithm provides high-precision predictions using time-lagged load variables and statistical modeling techniques.
The Autoregressive Moving Average Model (ARMA) in time series enables real-time forecasting with combined autoregressive and moving average components, implementable through statistical programming libraries.
MATLAB-based simulation, estimation, and forecasting implementation for autoregressive Markov switching models
The Kalman Filter is an "optimal recursive data processing algorithm" that provides the most efficient and effective solution for a wide range of problems. It has seen extensive applications for over 30 years in fields including robotic navigation, control systems, sensor data fusion, military radar systems, and missile tracking. In recent years, it has been increasingly applied to computer image processing tasks such as facial recognition, image segmentation, and edge detection. The filter operates through a two-step process: prediction (projecting state estimates forward) and update (correcting estimates with new measurements), typically implemented using matrix operations for state transition and covariance calculations.
The Autoregressive Markov Switching Model function is designed for evaluating, simulating, and forecasting autoregressive Markov switching models. It allows selection of appropriate distribution functions such as normal or t-distribution for model estimation. This tool is particularly valuable for investigating structural changes in time series data and supports research applications in finance, stock market analysis, and inflation studies. Key implementation aspects include state transition probability estimation and regime-dependent parameter specification.
Construction and analysis of time series models with detailed implementation approaches for ARMA(n,m) and AR model building, including practical code considerations.