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Memory Property of Grey Accumulation Generation Sequence

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  • Lifeng Wu
  • Xiaohui Gao
  • Yan Chen

Abstract

The influence of grey accumulation generation operator on the memory of time sequence is discussed. Generalized Hurst exponent (GHE) approach is used to calculate the memory in three cases, namely, the M3-Competition data, the air quality index in Beijing, Xingtai, and Handan, and the power generating capacity, and car production index in Hebei province. The result indicates that one order accumulation generation operator (1-AGO) can weaken the volatility and strengthen the memory of time sequence. It also explains the reason that one-order accumulation can be used in grey prediction.

Suggested Citation

  • Lifeng Wu & Xiaohui Gao & Yan Chen, 2019. "Memory Property of Grey Accumulation Generation Sequence," Complexity, Hindawi, vol. 2019, pages 1-10, July.
    • Handle: RePEc:hin:complx:9459038
    DOI: 10.1155/2019/9459038
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    References listed on IDEAS

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    1. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    2. Di Matteo, T. & Aste, T. & Dacorogna, M.M., 2003. "Scaling behaviors in differently developed markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 183-188.
    3. Ramos-Requena, J.P. & Trinidad-Segovia, J.E. & Sánchez-Granero, M.A., 2017. "Introducing Hurst exponent in pair trading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 488(C), pages 39-45.
    4. Che-Jung Chang & Jan-Yan Lin & Peng Jin, 2017. "A grey modeling procedure based on the data smoothing index for short-term manufacturing demand forecast," Computational and Mathematical Organization Theory, Springer, vol. 23(3), pages 409-422, September.
    5. Blachowicz, Tomasz & Ehrmann, Andrea & Domino, Krzysztof, 2016. "Statistical analysis of digital images of periodic fibrous structures using generalized Hurst exponent distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 167-177.
    6. Xu, Ning & Dang, Yaoguo & Gong, Yande, 2017. "Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China," Energy, Elsevier, vol. 118(C), pages 473-480.
    7. Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
    8. Lifeng Wu & Sifeng Liu & Ding Chen & Ligen Yao & Wei Cui, 2014. "Using gray model with fractional order accumulation to predict gas emission," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(3), pages 2231-2236, April.
    9. Ene, Seval & Öztürk, Nursel, 2017. "Grey modelling based forecasting system for return flow of end-of-life vehicles," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 155-166.
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