EOP time series prediction using singular spectrum analysis

Grigory Okhotnikov, Nina Golyandina

Research outputpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of MACLEAN: MAChine Learning for EArth ObservatioN Workshop
EditorsT. Corpetti, D. Ienco, R. Interdonato, et al.
PublisherRWTH Aahen University
Number of pages10
Publication statusPublished - 2019
Event2019 MAChine Learning for EArth ObservatioN Workshop, MACLEAN 2019 - Wurzburg
Duration: 20 Sep 2019 → …

Publication series

NameCEUR Workshop Proceedings
PublisherRWTH Aahen University
Volume2466
ISSN (Print)1613-0073

Conference

Conference2019 MAChine Learning for EArth ObservatioN Workshop, MACLEAN 2019
CountryGermany
CityWurzburg
Period20/09/19 → …

Scopus subject areas

  • Computer Science(all)

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