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Short-term prediction of UT1-UTC and LOD via Dynamic Mode Decomposition and combination of least-squares and vector autoregressive model

Michalczak Maciej, Ligas Marcin

Reports on Geodesy and Geoinformatics

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2024

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Vol. 117

45--54

EN

This study presents a short-term forecast of UT1-UTC and LOD using two methods, i.e. Dynamic Mode Decomposition (DMD) and combination of Least-Squares and Vector Autoregression (LS+VAR). The prediction experiments were performed separately for yearly time spans, 2018-2022. The prediction procedure started on January 1 and ended on December 31, with 7-day shifts between subsequent 30-day forecasts. Atmospheric Angular Momentum data (AAM) were used as an auxiliary time series to potentially improve the prediction accuracy of UT1-UTC and LOD in LS+VAR procedure. An experiment was also conducted with and without elimination of effect of zonal tides from UT1-UTC and LOD time series. Two approaches to using the best steering parameters for the methods were applied:. First, an adaptive approach, which observes the rule that before every single forecast, a preliminary one must be performed on the pre-selected sets of parameters, and the one with the smallest prediction error is then used for the final prediction; and second, an averaged approach, whereby several forecasts are made with different sets of parameters (the same parameters as in adaptive approach) and the final values are calculated as the averages of these predictions. Depending on the method and data combination mean absolute prediction errors (MAPE) for UT1-UTC vary from 0.63 ms to 1.43 ms for the 10th day and from 3.07 ms to 8.05 ms for the 30th day of the forecast. Corresponding values for LOD vary from 0.110 ms to 0.245 ms for the 10th day and from 0.148 ms to 0.325 ms for the 30th day.

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Forecasting seismic activity rates in northwest Himalaya through multivariate autoregressive forecast of seismicity algorithm

Chingtham Prasanta, Tiwari Anurag, Yadav Arabind Kumar

Acta Geophysica

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2019

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Vol. 67, no. 2

465--476

EN

In this study, a model based on multivariate autoregressive forecast of seismicity (MARFS) algorithm is adopted to forecast seismic activity rates in northwest Himalaya, using the compiled homogenized moment magnitude (MW) based catalogue. For this purpose, each source zone delineated by Yadav et al. (Pure Appl Geophys 170:283–295, 2012) is divided into a spatial grid interval of 0.5° × 0.5° while the entire catalogue span (1975–2010) is segregated into six time periods/grids to estimate seismic activity rates spatially and temporally. These seismic activity rates which are estimated from spatial density map of hypocenters exhibit high values in Chaman Fault (Zone 1), Hindukush-Pamir region (Zone 3) and the mega thrust systems, i.e., Main Central Thrust, Main Boundary Thrust and Himalayan Frontal Thrust (Zone 4). Then, the seismic activity rates during 2011–2016 could be forecasted by extrapolating (through auto-regression procedure) those observed for previous time periods. The forecast seismic activity rates are estimated within the values of 0 and 7.57 with high values primarily observed in Hindukush-Pamir region of Zone 3 and the gently north-dipping thrust fault systems (Main Central Thrust, Main Boundary Thrust, Himalayan Frontal Thrust) of Zone 4. Finally, the associated area under the curve of receiver operating characteristics graph suggests the superiority of forecasting model with respect to random prediction, whereas results of the data-consistency test, i.e., N test of our model, exhibit consistency in between the observed and simulated likelihoods. Moreover, the hypothetical t test performed in between the spatial grids of forecast seismic activity rates and observed seismic activity rates confirms that the former is consistent with the latter.

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UT1 prediction based on long-time series analysis

Tissen V., Tolstikov A., Malkin Z.

Artificial Satellites : Journal of Planetary Geodesy

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2010

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Vol. 45, No. 2

111--118

EN

A new method is developed for prediction of UT1. The method is based on construction of a general polyharmonic model of the Earth rotation parameters variations using all the data available for the last 80-100 years, and modified autoregression technique. A rigorous comparison of UT1 predictions computed at SNIIM with the prediction computed by IERS (USNO) in 2008-2009 has shown that proposed method provides better accuracy both for ultra-short and long term predictions.

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Wykorzystanie modeli autoregresji do prognozowania szeregu czasowego związanego ze sprzedażą asortymentu hutniczego

Szymszal J., Piątkowski J., Gierek A., Pucka A.

Archiwum Odlewnictwa

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2006

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R. 6, nr 18/1

39--44

PL

W artykule przestawiono metodykę prognozowania szeregu czasowego wykazującego wahania sezonowe, metodą autoregresji. Prognozowania dokonano dla szeregu czasowego, w czasie 5 dni (od poniedziałku do piątku), ujmującego wielkość sprzedaży wybranego asortymentu hutniczego. Przedstawiona metoda charakteryzuje się dużą dokładnością i może stanowić podstawę planowania wielkości sprzedaży, która jest nieodzownym czynnikiem współczesnych rozwiązań ekonomicznych.

EN

Presented in the article is a methodology of prognostication of the time series showing seasonal fluctuations, with applying an autoregression method. The prognostication has been made for the time series in the course of 5 days, from Monday to Friday, covering an amount of sales of a selected metallurgical product tem. The method presented is distinguished for a high accuracy and it can provide a basis be applied in planning the scale of sales which is an indispensable factor in modern economics solutions.