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EOP time series prediction using singular spectrum analysis. / Okhotnikov, Grigory; Golyandina, Nina.

Proceedings of MACLEAN: MAChine Learning for EArth ObservatioN Workshop. ed. / T. Corpetti; D. Ienco; R. Interdonato; et al. RWTH Aahen University, 2019. (CEUR Workshop Proceedings; Vol. 2466).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Okhotnikov, G & Golyandina, N 2019, EOP time series prediction using singular spectrum analysis. in T Corpetti, D Ienco, R Interdonato & et al. (eds), Proceedings of MACLEAN: MAChine Learning for EArth ObservatioN Workshop. CEUR Workshop Proceedings, vol. 2466, RWTH Aahen University, 2019 MAChine Learning for EArth ObservatioN Workshop, MACLEAN 2019, Wurzburg, Germany, 20/09/19.

APA

Okhotnikov, G., & Golyandina, N. (2019). EOP time series prediction using singular spectrum analysis. In T. Corpetti, D. Ienco, R. Interdonato, & et al. (Eds.), Proceedings of MACLEAN: MAChine Learning for EArth ObservatioN Workshop (CEUR Workshop Proceedings; Vol. 2466). RWTH Aahen University.

Vancouver

Okhotnikov G, Golyandina N. EOP time series prediction using singular spectrum analysis. In Corpetti T, Ienco D, Interdonato R, et al., editors, Proceedings of MACLEAN: MAChine Learning for EArth ObservatioN Workshop. RWTH Aahen University. 2019. (CEUR Workshop Proceedings).

Author

Okhotnikov, Grigory ; Golyandina, Nina. / EOP time series prediction using singular spectrum analysis. Proceedings of MACLEAN: MAChine Learning for EArth ObservatioN Workshop. editor / T. Corpetti ; D. Ienco ; R. Interdonato ; et al. RWTH Aahen University, 2019. (CEUR Workshop Proceedings).

BibTeX

@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",

}

RIS

TY - GEN

T1 - EOP time series prediction using singular spectrum analysis

AU - Okhotnikov, Grigory

AU - Golyandina, Nina

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Earth orientation parameters

KW - Forecasting

KW - Singular spectrum analysis

KW - Time series

UR - http://www.scopus.com/inward/record.url?scp=85073873703&partnerID=8YFLogxK

UR - http://ceur-ws.org/Vol-2466/paper1.pdf

UR - http://ceur-ws.org/Vol-2466/

M3 - Conference contribution

AN - SCOPUS:85073873703

T3 - CEUR Workshop Proceedings

BT - Proceedings of MACLEAN: MAChine Learning for EArth ObservatioN Workshop

A2 - Corpetti, T.

A2 - Ienco, D.

A2 - Interdonato, R.

A2 - et al.,

PB - RWTH Aahen University

T2 - 2019 MAChine Learning for EArth ObservatioN Workshop, MACLEAN 2019

Y2 - 20 September 2019

ER -

ID: 51231407