Extracting trend of time series based on improved empirical mode decomposition method

被引：0
|
作者：
Liu, Hui-ting [1 ,2 ]
Ni, Zhi-wei [1 ]
Li, Jian-yang [1 ,3 ]
机构：
[1] Hefei Univ Technol, Inst Comp Network Syst, Hefei 230009, Peoples R China
[2] Anhui Univ, Sch Comp Sci & Technol, Hefei 230039, Peoples R China
[3] Longyan Univ, Dept Comp Sci, Longyan 364000, Peoples R China
来源：
ADVANCES IN DATA AND WEB MANAGEMENT, PROCEEDINGS | 2007年 / 4505卷
关键词：
trend extraction; empirical mode decomposition; spline interpolation; overshoot and undershoot problems;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Solving overshoot and undershoot problems existed in the spline interpolation of empirical mode decomposition (EMD), improving this method and extracting trend of time series with it are the main tasks of this paper. A method is devised by using simple means of successive extrema instead from the envelope average to form the mean envelope. In this way, only one spline interpolation is required rather than two during the course of sifting process of EMD. It is easier to implement, those problems can be alleviated and EMD method is improved. How to get the successive extrema of series and how to realize trend extraction are also expounded in the paper. Experimental results show that the improved EMD method is better at trend extraction than the original one.
引用
收藏
页码：341 / +
页数：3
相关论文
共 50 条
  • [41] IMPROVED TIME DELAY ESTIMATION USING EMPIRICAL MODE DECOMPOSITION
    Harfas, Paul
    Ionel, Raul
    Gontean, Aurel
    [J]. 16TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MENDEL 2010, 2010, : 325 - 332
  • [42] Approach for Time Series Prediction Based on Empirical Mode Decomposition and Extreme Learning Machine
    Tian Zhongda
    Mao Chengcheng
    Wang Gang
    Ren Yi
    [J]. PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC), 2018, : 3119 - 3123
  • [43] On the prediction of the stock price based on empirical mode decomposition and deep time series model
    Lin Y.
    Chang J.
    Huang Y.
    [J]. Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice, 2022, 42 (06): : 1663 - 1677
  • [44] Failure rate time series prediction based on support vector empirical mode decomposition
    Zhang, Xian
    Wang, Hongli
    [J]. Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica, 2011, 32 (03): : 480 - 487
  • [45] A novel time-series model based on empirical mode decomposition for forecasting TAIEX
    Cheng, Ching-Hsue
    Wei, Liang-Ying
    [J]. ECONOMIC MODELLING, 2014, 36 : 136 - 141
  • [46] Analysis of Empirical Mode Decomposition-based Load and Renewable Time Series Forecasting
    Safari, N.
    Price, G. C. D.
    Chung, C. Y.
    [J]. 2020 IEEE ELECTRIC POWER AND ENERGY CONFERENCE (EPEC), 2020,
  • [47] Study on the phase-extracting method of self-mixing signal based on empirical mode decomposition
    Zhang Yu-Yan
    Zhou Hang
    Yan Meisu
    [J]. ACTA PHYSICA SINICA, 2015, 64 (05)
  • [48] An Improved Signal Processing Approach Based on Analysis Mode Decomposition and Empirical Mode Decomposition
    Chen, Zhongzhe
    Liu, Baqiao
    Yan, Xiaogang
    Yang, Hongquan
    [J]. ENERGIES, 2019, 12 (16)
  • [49] Improved Ensemble Empirical Mode Decomposition Method and Its Simulation
    Lin, Jinshan
    [J]. ADVANCES IN INTELLIGENT SYSTEMS, 2012, 138 : 109 - 115
  • [50] An Improved Empirical Mode Decomposition Method for Monitoring Electromechanical Oscillations
    Peng, J. C-H
    Kirtley, J. L., Jr.
    [J]. 2014 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT), 2014,
12345