摘要
近年来,学术和工业领域对时间序列数据的研究热潮不断增长,但其中蕴含的频率信息仍缺乏有效的建模。研究发现,时间序列预测依赖于不同的频率模式,为未来的趋势预测提供有用的线索:短期的序列预测更多依赖于高频分量,而长期预测则更多关注低频数据。为更好地挖掘时间序列的多频模式,提出了一个多特征自适应频域预测模型MAFD。该模型分为两个阶段:在第一阶段中,模型通过XGBoost算法对输入向量进行重要性度量,选择高重要性特征;在第二阶段,模型将时间序列的频率特征提取和目标序列的频域建模集成到一起,并根据时间序列对频率模式的依赖特点构建一个端到端的预测网络。MAFD的创新性体现在预测网络能够根据输入序列的动态演变自动关注不同的频率分量,从而揭示时间序列的多频模式,强化模型的学习能力。采用4种不同领域的数据集对模型进行了性能验证,实验结果表明,与现有经典的预测模型相比,MAFD具有更高的准确性和更小的滞后性。
In recent years,the research enthusiasm for time series data in academic and industrial fields has been increasing,but the frequency information contained in it still lacks effective modeling.The studies found that time series forecasting relies on different frequency patterns,providing useful clues for future trend forecasting:short-term series forecasting relies more on high-frequency components,while long-term forecasting focuses more on low-frequency data.In order to better mine the multi-frequency mode of time series,this paper proposes a multi-feature adaptive frequency domain prediction model(MAFD).MAFD is composed of two stages.In the first stage,it uses XGBoost algorithm to measure the importance of the input vector and selects high-importance features.In the second stage,the model integrates the frequency feature extraction of the time series and the frequency domain modeling of the target sequence,and builds an end-to-end prediction network based on the dependence of the time series on the frequency mode.The innovation of MAFD is reflected in the predictive network’s ability to automatically focus on diffe-rent frequency components according to the dynamic evolution of the input sequence,thereby revealing the multi-frequency pattern of the time series and strengthening the learning ability of the model.This work uses 4 datasets from different fields to verify the performance of the model.The experimental results show that compared with the existing classic prediction models,MAFD has higher accuracy and smaller lag.
作者
王晓迪
刘鑫
于晓
WANG Xiao-di;LIU Xin;YU Xiao(School of Public Finance and Taxation,Shandong University of Finance and Economics,Jinan 250014,China;School of Computer Science and Technology,Shandong University of Finance and Economics,Jinan 250014,China;Shandong Key Laboratory of Digital Media Technology,Jinan 250014,China)
出处
《计算机科学》
CSCD
北大核心
2021年第S02期204-210,共7页
Computer Science
基金
国家自然科学基金(62072274)。
