Neural Network Time Series Prediction with MATLAB Implementation

Neural network-based time series prediction methods have found extensive applications across multiple domains. Particularly, forecasting sunspot activity and rotor fault signals represents a significant research focus. In this study, we utilize MATLAB programming to analyze and predict both sunspot data and actual rotor fault signals as primary examples. The implementation involves crucial steps such as time series normalization using MATLAB's mapminmax function, neural network architecture configuration through the feedforwardnet or timedelaynet functions, and training optimization with the trainlm (Levenberg-Marquardt) algorithm. Through in-depth research and analysis of these signals' characteristics and variation patterns, we can enhance our understanding of their temporal behaviors and subsequently improve the accuracy and reliability of prediction models. The MATLAB code structure typically includes data segmentation into training and testing sets, network training with validation stopping criteria, and performance evaluation using metrics like mean squared error (MSE) and regression analysis.