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Języki publikacji
Abstrakty
A mathematical model known as one-order one-variable grey differential equation model GM(1, 1) has been herein employed successfully for the ultra short-term (<10days) predictions of universal time (UT1-UTC). The results of predictions are analyzed and compared with those obtained by other methods. It is shown that the accuracy of the predictions is comparable with that obtained by other prediction methods. The proposed method is able to yield an exact prediction even though only a few observations are provided. Hence it is very valuable in the case of a small size dataset since traditional methods, e.g., least-squares (LS) extrapolation, require longer data span to make a good forecast. In addition, these results can be obtained without making any assumption about an original dataset, and thus is of high reliability. Another advantage is that the developed method is easy to use. All these reveal a great potential of the GM(1, 1) model for UT1-UTC predictions.
Słowa kluczowe
Rocznik
Tom
Strony
19--29
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
autor
- National Time Service Center, Chinese Academy of Sciences, China
- Key Laboratory of Time and Frequency Primary Standards, Chinese Academy of Sciences, China
- University of Chinese Academy of Sciences, China
autor
- Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, China
autor
- National Time Service Center, Chinese Academy of Sciences, China
- University of Chinese Academy of Sciences, China
autor
- National Time Service Center, Chinese Academy of Sciences, China
- Key Laboratory of Time and Frequency Primary Standards, Chinese Academy of Sciences, China
autor
- Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, China
Bibliografia
- Akyilmaz, O. and Kutterer, H. (2004). Prediction of Earth Rotation Parameters by Fuzzy Inference Systems. Journal of Geodesy, 78(1-2), 82-93.
- Deng, J.L. (1986). Grey Forecasting and Decision. Huazhong University of Science and Technology Press, Wuhan, 97-134.
- Gambis, D. and Luzum, B. (2011). Earth Rotation Monitoring, UT1 Determination and Prediction. Metrologia, 48(4), 65-170.
- Gambis, D., Salstein, D.A. and Lambert, L. (2011). Use of Atmospheric Angular Momentum Forecasts for UT1 Predictions: Analyses over CONT08. Journal of Geodesy, 85(7), 435-441.
- Gross, R.S., Marcus, S.L., Eubanks, T.M., Dickey, J.O. and Keppenne, C.L. (1996). Detection of an ENSO Signal in Seasonal Length-of-day Variations. Geophysical Research Letters, 23(23), 3373-3376.
- Gross, R.S., Eubanks, T.M., Steppe, J.A., Freedman, A.P., Dickey, J.O. and Runge,T.F. (1998). A Kalman-filter-based Approach to Combining Independent Earth-orientation Series. Journal of Geodesy, 72(4), 215-235.
- Guo, J.Y., Li, Y.B., Dai, C.L. and Shum, C.K. (2013). A Technique to Improve the Accuracy of Earth Orientation Prediction Algorithms Based on Least Squares Extrapolation. Journal of Geodynamics, 70(10), 36-48.
- Kalarus, M., Schuh, H., Kosek, W., Akyilmaz, O. and Bizouard, Ch. (2010). Achievements of the Earth Orientation Parameters Prediction Comparison Campaign. Journal of Geodynamics, 84(10), 587-596.
- Kosek, W., McCarthy, D.D. and Luzum, B.J. (1998). Possible Improvement of Earth Orientation Forecast Using Autocovariance Prediction Procedures. Journal of Geodesy, 72(4), 189-199.
- Lei, Y., Zhao, D.N. and Cai, H.B. (2015a). Prediction of Length-of-day Using Extreme Learning Machine. Geodesy and Geodynamics, 6(2): 151-159.
- Lei, Y., Guo, M., Cai, H.B., Hu, D.D. and Zhao, D.N. (2015b). Prediction of Length-of-day Using Gaussian Process Regression. The Journal of Navigation, 68(3): 563-175.
- Liao, D.C., Wang, Q.J., Zhou, Y.H., Liao, X.H. and Huang, C.L. (2012). Long-term Prediction of the Earth Orientation Parameters by the Artificial Neural Network Technique. Journal of Geodynamics, 62: 87-92.
- Malkin, Z and Skurikhina, E. (1996). On Prediction of EOP. Communications of the Institute of Applied Astronomy RAS, No. 93.
- Niedzielski, T. and Kosek, W. (2008). Prediction of UT1–UTC, LOD and AAM χ3 by Combination of Least-squares and Multivariate Stochastic Methods. Journal of Geodesy, 82(2), 83-92.
- Petit, G. and Luzum, B. (2010). IERS Conventions (2010). IERS Technical Note No. 36, Verlag des Bundesamts für Kartographie und Geodäsie, Frankfurt am Main, 123-131.
- Schuh, H., Ulrich, M., Egger, D., Müller, J. and Schwegmann, W. (2002). Prediction of Earth Orientation Parameters by Artificial Neural Networks. Journal of Geodesy, 76(5), 247-258.
- Xu, X.Q. and Zhou, Y.H. (2015). EOP Prediction Using Least Square Fitting and Autoregressive Filter over Optimized Data Intervals. Advances in Space Research, in press.
- Zhang, X.H., Wang, Q.J., Zhu, J.J. and Zhang, H. (2012). Application of General Regression Neural Network to the Prediction of LOD Change. Chinese Astronomy and Astrophysics, 36(1), 86-96.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c372bfcf-cc6b-41f6-8f84-f30466a1f79c