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浙江大学学报(工学版)  2020, Vol. 54 Issue (12): 2414-2422    DOI: 10.3785/j.issn.1008-973X.2020.12.016
计算机与控制工程     
基于多层BiLSTM和改进粒子群算法的应用负载预测方法
蔡亮(),周泓岑,白恒,才振功*(),尹可挺,贝毅君
浙江大学 软件学院,浙江 宁波 315000
Application load forecasting method based on multi-layer bidirectional LSTM and improved PSO algorithm
Liang CAI(),Hong-cen ZHOU,Heng BAI,Zhen-gong CAI*(),Ke-ting YIN,Yi-jun BEI
College of Software Technology, Zhejiang University, Ningbo 315000, China
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摘要:

为了解决常用时序预测算法精度不高和调参困难的问题,提出基于多层双向长短期记忆(BiLSTM)神经网络的负载预测方法,包括网络模型设计、自适应参数设置和改进粒子群算法优化等步骤. 将数据输入网络模型中进行训练,使用自适应算法进行自动调参;采用基于基准模型的多指标融合的模型评价方法,计算改进粒子群算法的适应度;使用改进粒子群算法优化模型的预测结果. 通过与多种典型时间序列预测算法的实验对比,方法的预测平均绝对百分比误差减小3.6%~7.2%,训练时间缩短10%以上,实验结果验证了方法在时间序列预测中具有更高的准确性和很强的适用性,为使用负载预测结果进行弹性扩缩容提供了重要的科学依据.

关键词: 负载预测双向长短记忆(BiLSTM)粒子群算法(PSO)自适应算法多指标融合    
Abstract:

In order to solve the problems of low accuracy of commonly used time series prediction algorithms and difficulty in tuning parameters, a load prediction method was proposed based on a multilayer bidirectional long short-term memory (BiLSTM) neural network. The method includes network model design, adaptive parameter setting, and improved particle swarm optimization (PSO). The data was input into the BiLSTM network model for training and uses an adaptive algorithm for automatically parameter adjustment. The method uses a model evaluation method based on the benchmark model to calculate the fitness of the improved particle swarm optimization algorithm. The method uses improved particle swarm optimization to optimize the prediction results of the model. The average absolute percentage error of this method was reduced by 3.6% to 7.2% compared with several typical forecasting algorithms, and the training time was reduced by more than 10%, Through experimental comparison with a variety of typical time series prediction algorithms. Experimental results show that the method has higher accuracy and stronger applicability in time series forecasting, and provide an important scientific basis for the use of load forecast results for elastic expansion and contraction.

Key words: load forecaste    bidirectional long-short-term memory (BiLSTM)    particle swarm optimization (PSO)    adaptive algorithm    multi index fusion
收稿日期: 2019-12-31 出版日期: 2020-12-31
CLC:  TU 111  
基金资助: 浙江大学-中移在线联合创新实验室资助项目(CMOS01HT20180623);阿里巴巴-浙江大学前沿技术联合研发中心(XT622019000187)
通讯作者: 才振功     E-mail: leoncai@zju.edu.cn;cstcaizg@zju.edu.cn
作者简介: 蔡亮(1976—),男,博士,副教授,从事区块链、云计算、信息安全相关研究. orcid.org/0000-0001-7898-2372.E-mail: leoncai@zju.edu.cn
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引用本文:

蔡亮,周泓岑,白恒,才振功,尹可挺,贝毅君. 基于多层BiLSTM和改进粒子群算法的应用负载预测方法[J]. 浙江大学学报(工学版), 2020, 54(12): 2414-2422.

Liang CAI,Hong-cen ZHOU,Heng BAI,Zhen-gong CAI,Ke-ting YIN,Yi-jun BEI. Application load forecasting method based on multi-layer bidirectional LSTM and improved PSO algorithm. Journal of ZheJiang University (Engineering Science), 2020, 54(12): 2414-2422.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.12.016        http://www.zjujournals.com/eng/CN/Y2020/V54/I12/2414

图 1  LSTM记忆单元结构
图 2  BiLSTM网络结构
图 3  多层BiLSTM神经网络结构图
图 4  自适应调参算法流程图
图 5  请求量应用负载的单日变化
图 6  应用请求量负载的周变化
图 7  请求应用负载35天变化
模型 RMSE MAE MAPE/% Q
Pa-BiLSTM 121.05 78.67 3.97 0.37
PSO-BiLSTM 157.13 92.53 4.63 0.45
BiLSTM 176.25 105.24 5.65 0.49
Pa-LSTM 158.39 102.84 5.38 0.46
LSTM 267.40 155.03 7.64 0.65
ARIMA(1,1,1) 366.84 167.46 5.87 0.73
HoltWinters 196.13 162.49 8.21 0.58
Prophet 234.92 198.94 11.18 0.67
基准模型 452.22 347.03 29.62 1.00
表 1  负载预测中各种模型的预测误差
图 8  不同模型在不同数据规模下预测准确度
图 9  不同模型在不同数据规模下的训练时间
图 10  oPaBiLSTM与Pa-BiLSTM的适应度对比图
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