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An effective hybrid approach based on grey and ARMA for forecasting gyro drift

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  • Zhou, Zhi-Jie
  • Hu, Chang-Hua

Abstract

Gyro plays an important role in navigational systems and its drift has a direct influence on the precision. Therefore it is crucial that the gyro drift be forecasted precisely. In this paper, a hybrid modeling and forecasting approach based on the grey and the Box–Jenkins autoregressive moving average (ARMA) models is proposed to forecast the gyro drift. The results of experiments show that this method can forecast the drift precisely, which provides a basis for performance analysis and fault forecasting. Meanwhile, it can also be concluded that the hybrid method has a higher forecasting precision to the complex problems than the single method.

Suggested Citation

  • Zhou, Zhi-Jie & Hu, Chang-Hua, 2008. "An effective hybrid approach based on grey and ARMA for forecasting gyro drift," Chaos, Solitons & Fractals, Elsevier, vol. 35(3), pages 525-529.
    • Handle: RePEc:eee:chsofr:v:35:y:2008:i:3:p:525-529
    DOI: 10.1016/j.chaos.2006.05.039
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    Cited by:

    1. 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.
    2. Ayşe Soy Temür & Şule Yıldız, 2021. "Comparison of Forecasting Performance of ARIMA LSTM and HYBRID Models for The Sales Volume Budget of a Manufacturing Enterprise," Istanbul Business Research, Istanbul University Business School, vol. 50(1), pages 15-46, May.
    3. Mehdi Khashei & Zahra Hajirahimi, 2017. "Performance evaluation of series and parallel strategies for financial time series forecasting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-24, December.
    4. Liping Zhang & Li Wang & Yanling Zheng & Kai Wang & Xueliang Zhang & Yujian Zheng, 2017. "Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics," IJERPH, MDPI, vol. 14(3), pages 1-14, March.

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