文献类型：期刊文献

中文题名：基于时钟触发长短期记忆的多元时序预测

英文题名：Multivariate Time Series Prediction Based on Clockwork Triggered Long Short Term Memory

作者：冯勇[1];冯述放[1];罗娜[1]

机构：[1]华东理工大学能源化工过程智能制造教育部重点实验室,上海200237

年份：2023

卷号：49

期号：2

起止页码：255

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：Scopus;北大核心:【北大核心2020】；CSCD:【CSCD_E2023_2024】；

语种：中文

中文关键词：多元时间序列;循环神经网络;分频;稀疏结构;长短期记忆

外文关键词：multivariate time series;recurrent neural network;frequency division;sparse structure;long short term memory

摘要：在多元时间序列预测方法中,传统的模型无法敏锐地捕获时间序列短期突变信号从而导致预测趋势滞后和误差较大。本文提出了一种基于时钟触发长短期记忆(Clockwork Triggered Long Short Term Memory,CWTLSTM)网络的多元时序预测模型,通过增强对短期信息的捕获能力提高了预测精度。CWTLSTM将网络中所有的神经元进行分组,对每个分组赋予不同的激活频率,每一组神经元只在时间步长等于周期的整数倍时才被激活。根据周期是否为1将网络分为主干网络链和短期输入增强链,短期输入增强链在靠近输出位置的时间步上激活时,将输入信息的运算结果单向地传递给主干网络链,增强此时的输入权重,使模型在存储长期信息的基础上能快速响应短期突变信息带来的数据波动。在空气污染数据集和水泥篦冷机数据集上的验证结果表明,本文模型在减少预测误差与趋势判断上均有较好的表现。
In the existing multivariate time series prediction methods,traditional models cannot capture short-term mutation during long time series,which leads to the delay of prediction trend and large prediction error.This paper proposes a short-term information enhancement model,termed as clockwork triggered long short term memory(CWTLSTM)neural network.This new model groups neurons in the network and assigns different activation frequencies to each group.Each group of neurons is activated only when the time step is equal to an integer multiple of their specified period.According to whether the group period is 1,the network is divided into backbone network chain and short-term input enhancement chain.When the short-term input enhancement chain is activated on the time step close to the output position,the operation result of the input information at that point is transmitted to the backbone network chain in one direction to enhance the weight of short-term input data.Thus,this model can quickly respond to the data fluctuation caused by short-term mutation information on the basis of storing long-term information.Compared with LSTM,XGboost and CWRNN models,the prediction performance of CWTLSTM is verified by air pollution data set and cement cooler data set.It is shown via experiment results that the proposed model has good performance in reducing forecasting error and forecasting future trend.In the experiment,the parameter sensitivity of the model to the periodic allocation strategy is also analyzed and the role of CWTLSTM in short-term information enhancement is illustrated.