Research on a Time Series Forecasting Model Based on Multiple Regression and Polynomial Fitting - Wang, Junran (Transactions on Computer Science and Intelligent Systems Research, 2025)
To address the inherent limitations of traditional time series forecasting models in handling complex real-world scenarios characterized by nonlinear dynamics and multivariate interactions, this paper proposes a novel hybrid prediction framework that synergistically integrates multivariate regression analysis with adaptive polynomial fitting mechanisms. First, outliers and missing values are processed through data cleaning technology. A feature system of many parameters such as event focus, new event participation rate, and host advantage growth coefficient is constructed. A prediction model based on polynomial regression is designed. And the parameter combination is optimized through cross-validation. The experimental results show that the mean square error of the model on the standard data set is lower than that of the traditional method, and the mean absolute error is reduced. The research results verify the effectiveness of the multi-model fusion strategy in nonlinear time series prediction and provide a new method for processing time series data.