@inproceedings{1ac12a4310e143c4a5aff6669ec5dc88,

title = "EOP time series prediction using singular spectrum analysis",

abstract = "Accurate forecasting of Earth orientation parameters (EOP) is important for improving the GPS location accuracy and navigation of Earth satellites. EOP time series include periodic components of complex structure. Singular Spectrum Analysis (SSA) is a nonparametric method that is capable of decomposing and forecasting time series with sine-wave components. In the paper, a unified approach to choosing parameters of the SSA forecasting algorithm for EOP time series prediction is proposed. EOP time series data published by IERS in Bulletin 14 C04 are used for 365-days prediction. The forecasts performed by the proposed techniques are compared with predictions taken from available public sources.",

keywords = "Earth orientation parameters, Forecasting, Singular spectrum analysis, Time series",

author = "Grigory Okhotnikov and Nina Golyandina",

year = "2019",

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "RWTH Aahen University",

editor = "T. Corpetti and D. Ienco and R. Interdonato and {et al.}",

booktitle = "Proceedings of MACLEAN: MAChine Learning for EArth ObservatioN Workshop",

address = "Germany",

note = "2019 MAChine Learning for EArth ObservatioN Workshop, MACLEAN 2019 ; Conference date: 20-09-2019",

}