IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v42y2009i2p1003-1009.html
   My bibliography  Save this article

GM(1,1) grey prediction of Lorenz chaotic system

Author

Listed:
  • Zhang, Yagang
  • Xu, Yan
  • Wang, Zengping

Abstract

The grey prediction of Lorenz chaotic system will be discussed carefully in this paper. We are mainly using GM(1,1) model to predict data sequences, and the usual prediction precision has exceeded 90%. In the symbolic prediction of Lorenz chaotic dynamical system, the precision of grey prediction certainly will decrease as the length of symbolic sequence is increasing. But in this place we have found a generating rule that may realize chaotic synchronization at least in a short and medium term, and we can analysis and predict in this way.

Suggested Citation

  • Zhang, Yagang & Xu, Yan & Wang, Zengping, 2009. "GM(1,1) grey prediction of Lorenz chaotic system," Chaos, Solitons & Fractals, Elsevier, vol. 42(2), pages 1003-1009.
    • Handle: RePEc:eee:chsofr:v:42:y:2009:i:2:p:1003-1009
    DOI: 10.1016/j.chaos.2009.02.031
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077909000848
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2009.02.031?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. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shalini Shekhawat & Akash Saxena & Ramadan A. Zeineldin & Ali Wagdy Mohamed, 2023. "Prediction of Infectious Disease to Reduce the Computation Stress on Medical and Health Care Facilitators," Mathematics, MDPI, vol. 11(2), pages 1-18, January.

    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. Li, Der-Chiang & Chang, Che-Jung & Chen, Chien-Chih & Chen, Wen-Chih, 2012. "Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case," Omega, Elsevier, vol. 40(6), pages 767-773.
    2. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    3. Shujaat Abbas & Hazrat Yousaf & Shabeer Khan & Mohd Ziaur Rehman & Dmitri Blueschke, 2023. "Analysis and Projection of Transport Sector Demand for Energy and Carbon Emission: An Application of the Grey Model in Pakistan," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    4. Zhou, Dengji & Yu, Ziqiang & Zhang, Huisheng & Weng, Shilie, 2016. "A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation," Energy, Elsevier, vol. 109(C), pages 420-429.
    5. Yang, Yang & Xue, Dingyü, 2016. "Continuous fractional-order grey model and electricity prediction research based on the observation error feedback," Energy, Elsevier, vol. 115(P1), pages 722-733.
    6. Dilaver, Zafer & Hunt, Lester C., 2011. "Turkish aggregate electricity demand: An outlook to 2020," Energy, Elsevier, vol. 36(11), pages 6686-6696.
    7. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
    8. Lee, Yi-Shian & Tong, Lee-Ing, 2012. "Forecasting nonlinear time series of energy consumption using a hybrid dynamic model," Applied Energy, Elsevier, vol. 94(C), pages 251-256.
    9. Wu, Qunli & Peng, Chenyang, 2017. "A hybrid BAG-SA optimal approach to estimate energy demand of China," Energy, Elsevier, vol. 120(C), pages 985-995.
    10. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    11. NGO, Hoang Anh & HOANG, Thai Nam, 2020. "A Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1,1) and application in predicting total COVID-19 cases," OSF Preprints 6y95m, Center for Open Science.
    12. Yu-Feng Zhao & Ming-Huan Shou & Zheng-Xin Wang, 2020. "Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models," IJERPH, MDPI, vol. 17(12), pages 1-20, June.
    13. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    14. Mehmet Kayakuş, 2020. "The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 227-236, December.
    15. Kucukali, Serhat & Baris, Kemal, 2010. "Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach," Energy Policy, Elsevier, vol. 38(5), pages 2438-2445, May.
    16. Dutta, Goutam & Mitra, Krishnendranath, 2015. "Dynamic Pricing of Electricity: A Survey of Related Research," IIMA Working Papers WP2015-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    17. Erdogdu, Erkan, 2010. "Natural gas demand in Turkey," Applied Energy, Elsevier, vol. 87(1), pages 211-219, January.
    18. Shalini Shekhawat & Akash Saxena & Ramadan A. Zeineldin & Ali Wagdy Mohamed, 2023. "Prediction of Infectious Disease to Reduce the Computation Stress on Medical and Health Care Facilitators," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    19. Wang, Yuanyuan & Wang, Jianzhou & Zhao, Ge & Dong, Yao, 2012. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," Energy Policy, Elsevier, vol. 48(C), pages 284-294.
    20. Weiwei Pan & Lirong Jian & Tao Liu, 2019. "Grey system theory trends from 1991 to 2018: a bibliometric analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1407-1434, December.

    More about this item

    Statistics

    Access and download statistics

    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:chsofr:v:42:y:2009:i:2:p:1003-1009. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.