Mean Generating Function Time Series Prediction Algorithm Program

This document presents a time series prediction algorithm program based on the mean generating function, accompanied by detailed specifications for three key program files. The implementation consists of a main program predict_fun.m that handles the core prediction workflow, along with two supporting subroutines: timeseries.m for time series data preprocessing and normalization, and seriesexpan.m responsible for series expansion operations and feature generation. These programs enable users to efficiently apply the algorithm for forecasting time series trends and future developments. The algorithm utilizes mean generating functions to capture underlying patterns in sequential data, employing sliding window techniques for local averaging and trend extraction. Additionally, we provide comprehensive documentation covering the algorithm's theoretical foundation, implementation methodology, and practical application scenarios to help users fully understand the principles and optimal use cases. We believe that through studying and implementing this algorithm, users can enhance their time series forecasting capabilities and make more informed data-driven decisions.