MATLAB Implementation of Embedding Dimension Selection Based on Chaotic Time Series

In short-term load forecasting using chaos theory and neural networks, the selection of neural network inputs is critically important. This MATLAB program implements an embedding dimension selection algorithm based on chaotic time series analysis. The algorithm analyzes chaotic time series characteristics to determine the optimal embedding dimension, thereby improving the accuracy and reliability of load forecasting. The implementation includes key functions for phase space reconstruction and correlation dimension calculation, supporting multiple methods for neural network input selection to accommodate different requirements and conditions in load forecasting tasks. By integrating chaos theory with neural networks, this approach enables more effective short-term load predictions, providing valuable support for energy management and power system operations. The program utilizes MATLAB's mathematical computing capabilities for efficient time series processing and dimension optimization calculations.