Explore MATLAB source code curated for "时间序列预测" with clean implementations, documentation, and examples.
MATLAB-based source code for ARIMA (AutoRegressive Integrated Moving Average) model. This implementation provides comprehensive tools for time series analysis, featuring model building procedures and spectral analysis capabilities. The code handles parameter estimation, differencing operations, and forecasting functions with configurable parameters for customized time series predictions.
A novel time series forecasting algorithm utilizing cloud models for short-term electricity price prediction, featuring enhanced accuracy through digital cloud characteristic modeling.
MATLAB implementation of statistical chaos methods for nonlinear system time series prediction with code-based algorithm explanations
This project utilizes historical wind power data to forecast future power values through MATLAB programming and Excel data processing. It implements neural network simulation prediction, gray prediction, and time series forecasting methods to identify patterns in historical data for wind power prediction. The forecasted results undergo error analysis and benchmarking against established standards to evaluate prediction reliability, with detailed algorithmic implementations in MATLAB for each method.
MATLAB-based SVM program for time series forecasting with enhanced algorithmic implementation details
Time series data represents sequences that change randomly over time, where forecasting involves using historical data points to predict current and future values. Traditional time series prediction methods in stochastic process theory typically employ linear models such as AR, MA, and ARMA models. However, these models require manual selection of model types and determination of orders, often resulting in significant prediction errors. Wavelet theory, an emerging mathematical method, has gained prominence in recent years. Wavelet neural networks combine wavelet analysis with neural networks to effectively address time series prediction challenges. This case study demonstrates the application of wavelet neural networks for traffic flow prediction, highlighting their effectiveness in time series forecasting through practical implementation and algorithm validation.
Time series prediction implementation using wavelet neural networks with MATLAB code
Time series refers to a sequence of data points arranged at specific time intervals, representing various metrics such as product demand, production volume, or sales figures. The intervals can be measured in any time unit (hours, days, weeks, months). When establishing relationships with dependent variables proves difficult or data collection is challenging, regression analysis may not be suitable. For cases where high prediction accuracy isn't critical, time series analysis offers an effective alternative. Implementation typically involves preprocessing data using wavelet decomposition (e.g., MATLAB's wavedec function) to extract features, followed by neural network training with functions like feedforwardnet for pattern recognition and forecasting.
Implementing time series forecasting with Echo State Neural Networks - demonstrating excellent prediction performance through reservoir computing methodology
MATLAB-based SVM program for time series prediction featuring multiple forecasting models with detailed algorithm implementation