IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v534y2019ics0378437119312592.html
   My bibliography  Save this article

Experimental investigation of empirical mode decomposition by reduction of end effect error

Author

Listed:
  • Massouleh, S.H. Momeni
  • Kordkheili, S.A. Hosseini

Abstract

Empirical mode decomposition as a complete data driven method is typically employed to obtain constitutive components of all kinds of signal including non-stationary and nonlinear. Extracting modes using this method is normally associated with multiple sources of errors such as stop criteria, end effects, interpolation function and etc. In order to reduce end effects errors in this paper a modified method is proposed based on combination of auto regressive model and mirror method. In this combination method, to extract some of first intrinsic mode functions of a given signal, auto regressive model is initially implemented to forecast tails of maximum and minimum envelops for only a short section of the signal at its both ends. Then the mirror method is employed to continue sifting process for remaining signal that has no enough extrema to employ auto regressive model. Advantages of the proposed combined method are analytically and experimentally assessed and comparisons with mirror method solutions are presented.

Suggested Citation

  • Massouleh, S.H. Momeni & Kordkheili, S.A. Hosseini, 2019. "Experimental investigation of empirical mode decomposition by reduction of end effect error," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    • Handle: RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119312592
    DOI: 10.1016/j.physa.2019.122171
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119312592
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.122171?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Norden E. Huang & Man‐Li Wu & Wendong Qu & Steven R. Long & Samuel S. P. Shen, 2003. "Applications of Hilbert–Huang transform to non‐stationary financial time series analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 19(3), pages 245-268, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mahata, Ajit & Rai, Anish & Nurujjaman, Md. & Prakash, Om, 2021. "Modeling and analysis of the effect of COVID-19 on the stock price: V and L-shape recovery," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    2. Yuan-Chien Lin & En-Dian Kuo & Wan-Ju Chi, 2021. "Analysis of Meteorological Drought Resilience and Risk Assessment of Groundwater Using Signal Analysis Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 179-197, January.
    3. Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
    4. Xinchang Liu & Bolong Liu, 2023. "A Hybrid Time Series Model for Predicting the Displacement of High Slope in the Loess Plateau Region," Sustainability, MDPI, vol. 15(6), pages 1-26, March.
    5. Shao, Liuguo & Zhang, Hua, 2020. "The impact of oil price on the clean energy metal prices: A multi-scale perspective," Resources Policy, Elsevier, vol. 68(C).
    6. Rai, Anish & Mahata, Ajit & Nurujjaman, Md & Majhi, Sushovan & Debnath, Kanish, 2022. "A sentiment-based modeling and analysis of stock price during the COVID-19: U- and Swoosh-shaped recovery," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    7. Wang, Haoyu & Di, Junpeng & Yang, Zhaojun & Han, Qing, 2020. "Assessment of mutual fund performance based on Ensemble Empirical Mode Decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    8. Liu, Shuyu & Huang, Shupei & Chi, Yuxi & Feng, Sida & Li, Yang & Sun, Qingru, 2020. "Three-level network analysis of the North American natural gas price: A multiscale perspective," International Review of Financial Analysis, Elsevier, vol. 67(C).
    9. Korotin, Vladimir & Dolgonosov, Maxim & Popov, Victor & Korotina, Olesya & Korolkova, Inna, 2019. "The Ukrainian crisis, economic sanctions, oil shock and commodity currency: Analysis based on EMD approach," Research in International Business and Finance, Elsevier, vol. 48(C), pages 156-168.
    10. Ftiti, Zied & Hadhri, Sinda, 2019. "Can economic policy uncertainty, oil prices, and investor sentiment predict Islamic stock returns? A multi-scale perspective," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 40-55.
    11. Jin, Xuejun & Zhu, Keer & Yang, Xiaolan & Wang, Shouyang, 2021. "Estimating the reaction of Bitcoin prices to the uncertainty of fiat currency," Research in International Business and Finance, Elsevier, vol. 58(C).
    12. Cho, Jung-Hoon & Kim, Dong-Kyu & Kim, Eui-Jin, 2022. "Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    13. Mahata, Ajit & Bal, Debi Prasad & Nurujjaman, Md, 2020. "Identification of short-term and long-term time scales in stock markets and effect of structural break," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    14. Ouyang, Fang-Yan & Zheng, Bo & Jiang, Xiong-Fei, 2019. "Dynamic fluctuations of cross-correlations in multi-time scale," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 515-521.
    15. Zhongqi Shi & Rumian Zhong & Nan Jin, 2023. "Real-Time Tracking of Time-Varying Cable Frequency Based on a Time-Domain Signal Processing Method," Sustainability, MDPI, vol. 15(2), pages 1-12, January.
    16. Chen, Mu-Chen & Wei, Yu, 2011. "Exploring time variants for short-term passenger flow," Journal of Transport Geography, Elsevier, vol. 19(4), pages 488-498.
    17. Shomesh E. Chaudhuri & Andrew W. Lo, 2019. "Dynamic Alpha: A Spectral Decomposition of Investment Performance Across Time Horizons," Management Science, INFORMS, vol. 65(9), pages 4440-4450, September.
    18. Tim Leung & Theodore Zhao, 2021. "Multiscale Decomposition and Spectral Analysis of Sector ETF Price Dynamics," JRFM, MDPI, vol. 14(10), pages 1-22, October.
    19. Huang, Junwei & Xiao, Qingtai & Liu, Jingjing & Wang, Hua, 2019. "Modeling heat transfer properties in an ORC direct contact evaporator using RBF neural network combined with EMD," Energy, Elsevier, vol. 173(C), pages 306-316.
    20. Azim Heydari & Meysam Majidi Nezhad & Mehdi Neshat & Davide Astiaso Garcia & Farshid Keynia & Livio De Santoli & Lina Bertling Tjernberg, 2021. "A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data," Energies, MDPI, vol. 14(12), pages 1-13, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119312592. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.