计算机工程与应用2025，Vol.61Issue(10)：120-132,13.DOI:10.3778/j.issn.1002-8331.2401-0193
面向长期时间序列预测的多项式投影与信息交换架构
Polynomial Projection and Information Exchange Architecture for Long-Term Time Series Forecasting
摘要
Abstract
Long-term time series forecasting utilizes historical data to predict the trends of future sequences over extended periods,providing support for long-term warning,planning,and decision-making.Existing methods commonly encounter issues of distribution shifts and long-term dependencies that are difficult to capture when conducting long-term forecasting.This paper proposes a Legendre polynomial projection and information exchange architecture(LPPIEA)tailored for long-term time series forecasting.Reversible instance data normalization is introduced to reduce the influence of distribu-tion shifts in long-term time series on predictions.Legendre polynomial projection is employed to handle complex tempo-ral patterns,acquiring high-dimensional feature representations of the data to enhance the model inference capability for long-term time series.To effectively capture long-term temporal dependencies,a lightweight information exchange archi-tecture is constructed to efficiently capture such dependencies,thus achieving accurate and efficient long-term time series forecasting.Experimental results on four commonly used public datasets demonstrate that the LPPIEA model reduces pre-diction errors by an average of 11.4%compared to baseline methods,while exhibiting higher computational efficiency.关键词
时间序列预测/长期时间依赖/多项式投影/信息交换架构/深度学习Key words
time series forecasting/long-term temporal dependencies/polynomial projection/information exchange architecture/deep learning分类
信息技术与安全科学引用本文复制引用
刘建鑫,马廷淮,苏昱铭,荣欢..面向长期时间序列预测的多项式投影与信息交换架构[J].计算机工程与应用,2025,61(10):120-132,13.基金项目
国家重点研发计划(政府间国际合作项目)(2021YFE010-4400) （政府间国际合作项目）
国家自然科学基金(62102187). （62102187）
