Research on the Advantages of Hybrid Forecasting Method and Its Performance Under Different Forecasting Spans

Abstract

Prediction technology is the key support for decision-making in the fields of economy, energy, environment and so on, but there are obvious limitations in a single prediction method: ARIMA and other statistical models are difficult to fit nonlinear data, and LSTM and other machine learning models rely on large samples and are easy to over fit. Through literature analysis and method comparison, combined with typical scenarios such as power load, GDP growth, population planning, this paper focuses on the core advantages of the hybrid forecasting method, and explores its performance in the short-term (1-3 months), medium-term (3-12 months), and long-term (more than 1 year) forecasting span. The results show that the hybrid method breaks through the accuracy bottleneck and enhances robustness by "basic model complementarity+fusion strategy optimization": it is good at capturing real-time fluctuations in short-term forecasting, and can balance stability and dynamic adaptability in medium and long-term forecasting. The research provides practical reference for the selection of prediction methods in different scenarios, and effectively avoids the scene adaptation defects of a single method.