Time series online prediction method based on information perception weight and error prediction

被引：0
|
作者：
Wang, Hao [1 ,2 ]
Liu, Zhen [1 ]
机构：
[1] School of Automation Engineering, University of Electronic science and Technology of China, Chengdu,611731, China
[2] Unit 91388 of the PLA, Zhanjiang,524022, China
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2020年 / 41卷 / 11期
关键词：
Aiming at the problems of insufficient perception of changes in data characteristics and the insufficient timeliness of prediction in existing time series online prediction methods; this article innovatively designs a time series online prediction method based on information perception weight and error prediction. The method uses the information perception weight to replace the forgetting factor parameter λ0 in the cost function; through establishing the mapping relationship between input data and prediction error; error prediction is performed; then error compensation is realized using weighting error compensation coefficient. Multiple single-step prediction experiments were carried out through using the method of changing the number of hidden layer nodes. The experiment results verify the excellent single-step prediction ability of the design method in terms of prediction accuracy and generalization. Among them; the single-step prediction variances of Sinc; Mackey-Glass and Solar Energy that are the three data selection points reach 1.56×10-13; 2.29×10-7; and; 1.43; respectively. According to the actual failure situation; the failure voltage was set to 5.8V and 5.6V; respectively; multiple-step prediction was performed aiming at the actually measured data in the accelerated life test of the packaged step-down power module. The five-step and ten-step prediction results show that the design method can effectively predict power failure. The experiment results fully demonstrate that the design method can complete online single-step and multi-step predictions stably; accurately and effectively when the predicted data characteristics are changed. © 2020; Science Press. All right reserved;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：31 / 41
相关论文
共 50 条
  • [21] Online ARIMA Algorithms for Time Series Prediction
    Liu, Chenghao
    Hoi, Steven C. H.
    Zhao, Peilin
    Sun, Jianling
    THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2016, : 1867 - 1873
  • [22] Online prediction of time series with assumed behavior
    Rosenfeld, Ariel
    Cohen, Moshe
    Kraus, Sarit
    Keshet, Joseph
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2020, 88
  • [23] Online Time Series Prediction with Missing Data
    Anava, Oren
    Hazan, Elad
    Zeevi, Assaf
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 37, 2015, 37 : 2191 - 2199
  • [24] Online Prediction of Time Series Data With Kernels
    Richard, Cedric
    Bermudez, Jose Carlos M.
    Honeine, Paul
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2009, 57 (03) : 1058 - 1067
  • [25] Processing of probabilistic information in weight perception and motor prediction
    Leif Trampenau
    Thilo van Eimeren
    Johann Kuhtz-Buschbeck
    Attention, Perception, & Psychophysics, 2017, 79 : 404 - 414
  • [26] Processing of probabilistic information in weight perception and motor prediction
    Trampenau, Leif
    van Eimeren, Thilo
    Kuhtz-Buschbeck, Johann
    ATTENTION PERCEPTION & PSYCHOPHYSICS, 2017, 79 (02) : 404 - 414
  • [27] Online Sequential Extreme Learning Machine Based Multilayer Perception with Output Self Feedback for Time Series Prediction
    潘峰
    赵海波
    Journal of Shanghai Jiaotong University(Science), 2013, 18 (03) : 366 - 375
  • [28] Online sequential extreme learning machine based multilayer perception with output self feedback for time series prediction
    Pan F.
    Zhao H.-B.
    Journal of Shanghai Jiaotong University (Science), 1600, Shanghai Jiaotong University (18): : 366 - 375
  • [29] A Survey of Time Series Online Prediction Based on Kernel Adaptive Filters
    Han M.
    Ma J.-Z.
    Ren W.-J.
    Zhong K.
    Zidonghua Xuebao/Acta Automatica Sinica, 2021, 47 (04): : 730 - 746
  • [30] Information diffusion prediction based on time-series analysis and information novelty
    Cai, Fei
    Chen, Hong-Hui
    Shu, Zhen
    Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications, 2013, 36 (01): : 59 - 62
12345