CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction

作     者：Xiao-Qian Lu Jun Tian Qiang Liao Zheng-Wu Xu Lu Gan Xiao-Qian Lu;Jun Tian;Qiang Liao;Zheng-Wu Xu;Lu Gan

作者机构：Bidding and Purchasing CenterUniversity of Electronic Science and Technology of ChinaChengdu611731China Yibin Research InstituteUniversity of Electronic Science and Technology of ChinaYibin644000China School of Information and Communication EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China 

出 版 物：《Journal of Electronic Science and Technology》 (电子科技学刊（英文版）)

年 卷 期：2024年第22卷第2期

页      面：77-90页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学] 

主　　题：Chaotic time series Incremental attention mechanism Phase-space reconstruction 

摘      要：To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention ***,a traversal search is conducted through the traversal layer for finite parameters in the phase ***,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input ***,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction *** model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space ***,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction *** experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction ***,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space *** experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.