摘要
传统时序预测方法其预测过程无法在相同数据集上推出共享模式,而机器学习方法无法较好地处理非线性和大规模数据集,并且需要手动设计特征工程.深度学习方法弥补了传统预测方法需要高计算高人力的弊端,用自动学习特征工程代替了手动设计特征工程.但仅使用深度学习的预测方法所作结构假设较少,通常需要较高的计算资源以及大量的数据来学习得到准确的模型.针对上述问题,本文提出通过采用融合t检验的EMD经验模态将序列分为高频分量和低频分量,对高频分量使用传统STL序列分解方法进一步对数据做处理,对高频、低频分量分别进行Prophet预测.实验结果表明,相较于传统的LSTM以及Prophet预测模型,经过STL序列分解后的周期数据能够提升模型的整体预测精确度而融合EMD经验模态的Prophet模型则大大提升了训练效率.
The prediction process of traditional time series prediction methods cannot push out the sharing mode on the same data set,while machine learning methods fail to handle nonlinear and large-scale data sets well,and feature engineering needs to be designed manually.The deep learning method makes up for the disadvantage of the traditional prediction method that requires high computation and high manpower,and it uses automatic learning feature engineering instead of manually designed feature engineering.However,prediction methods that only use deep learning make fewer structural assumptions and typically require higher computational resources and a large amount of data to learn accurate models.In response to the above issues,this study proposes to use empirical mode decomposition(EMD)fusing t-tests to divide the sequence into high-frequency and low-frequency components and further process the data using traditional standard template library(STL)sequence decomposition methods for high-frequency components.The high-frequency and low-frequency components are predicted separately by Prophet.The experimental results show that compared with traditional long short-term memory(LSTM)network and Prophet prediction models,the periodic data decomposed by STL sequence can improve the overall prediction accuracy of the model,while the Prophet model fused with EMD greatly improves training efficiency.
作者
丁美荣
张迎春
DING Mei-Rong;ZHANG Ying-Chun(School of Software,South China Normal University,Foshan 528225,China)
出处
《计算机系统应用》
2023年第11期294-301,共8页
Computer Systems & Applications
关键词
STL
时间序列
时序预测
序列分解
经验模态分解
standard template library(STL)
time series
time series prediction
series decomposition
empirical mode decomposition(EMD)
