Short-Term Polar Motion Forecast Based on the Holt-Winters Algorithm and Angular Momenta of Global Surficial Geophysical Fluids

Luo, Jiangshui; Liu, Chengli; Ray, Jim; Li, Jiancheng

doi:10.1007/s10712-022-09733-0

Cited by 8 publications

(4 citation statements)

References 57 publications

(63 reference statements)

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“…This is the last stage which shows that the modeling results of the six stocks affiliated with Israel using the non-seasonal Holt-Winters method can be categorized as very good. From this result, it can provide good forecasting results for some time to come, especially in the context of adjusting the Holt-Winters method is more suitable for short-term forecasting (Luo et al, 2022). For this reason, in this study, log return forecasting is carried out for the next 5 days for the six stocks.…”

Section: Figure 1 : Plot Of Closing Stock Prices Of 6 Company Stocksmentioning

confidence: 99%

The Non-Seasonal Holt-Winters Method for Forecasting Stock Price Returns of Companies Affected by BDS Action

Qur'ani,

Widyaningrum

2024

Mikailalsys J. of Math. and Statistics

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The non-seasonal Holt-Winters method is one of the methods of smoothing theory. This method can be implemented on time series data that does not have a seasonal component. In this study, this method is used to forecast the stock price returns of companies affected by the Boycott, Divestment, and Sanctions (BDS) action. Forecasting gets very good results that can be seen from the MAPE value of modeling the six stocks affiliated with Israel that continue to carry out Zionism against Palestine is not more than 10%. This method can also accommodate the limitations of existing data while still obtaining good forecasting results. In addition, the use of several transformations of stock price returns in this case is very useful in modeling to obtain appropriate error assumptions. The forecasting results of the model formed as a whole follow the trend in the stock price of each company. To produce good forecasting results using this method, it is recommended to do forecasting in the short term. The forecasting results show that of the six company stocks, almost all of them experienced a decrease in stock price returns. Only one stock of PT Map Boga Adiperkasa Tbk has increased. This also illustrates that the BDS action influences on these companies.

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Section: Figure 1 : Plot Of Closing Stock Prices Of 6 Company Stocksmentioning

confidence: 99%

The Non-Seasonal Holt-Winters Method for Forecasting Stock Price Returns of Companies Affected by BDS Action

Qur'ani,

Widyaningrum

2024

Mikailalsys J. of Math. and Statistics

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“…The data for this study were taken from the IERS, which publishes the IERS 14C04 series and forecast product Bulletin-A. Among them, the IERS 14C04 PM data are used as the truth reference to test proposed method (Luo et al 2022), Bulletin-A is used as one of the comparison method. The PMX and PMY series from January 6, 2002 to January 1, 2021, were chosen as experimental data.…”

Section: Datasetsmentioning

confidence: 99%

A new medium-long term polar motion prediction method based on sliding average within difference series

Wang

Miao

2023

Meas. Sci. Technol.

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Users such as satellite navigation orbiting and deep space exploration are demanding more and more accuracy in polar motion (PM) forecasting, while the existing PM forecasting methods are limited in the single input data and forecasting strategy. To address the problem, this study proposes a new idea of PM forecasts by combining the differences between PM series and the sliding average within the PM series. We use the LS+AR models driven by IERS-14C04 series, to perform experiments with the traditional method and the sliding average method within the PMX, PMY and PM(Y-X) series, respectively. Among them, the prediction of PMX is obtained by subtracting the forecast of PMY of traditional method and the prediction of PM(Y-X) of the sliding average method, the forecast of PMY is obtained by adding the forecast of PMX of the sliding average method and the forecast of PM(Y-X) of the traditional method. The results of the 418-week hindcast experiment from 2012 to 2021 show that the proposed method has a greater improvement than the traditional method, and the corresponding 1-365-day forecasting accuracies are improved by 31.46% and 21.11% over the traditional method, respectively, on average. It has certain advantages over the IERS Bulletin-A in the medium-long term. This not only verifies that the stability and ability of the method have some competitive, but also provides a new idea for the research of PM prediction.

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“…Furthermore, only AAM is used, which is essentially one type of the effective angular momentum (EAM) functions (Barnes et al., 1983). It is shown that oceanic angular momentum (OAM), hydrological angular momentum (HAM), and sea level angular momentum (SLAM) can have a nonnegligible effect on polar motion and dUT1 as well (Bizouard & Seoane, 2010; Brzezinski & Nastula, 2002; Chin et al., 2004; Dahlen, 1976; Dill & Dobslaw, 2010; Dobslaw et al., 2010; Gross, 2008; Kiani Shahvandi, Schartner, & Soja, 2022; Luo et al., 2022; Nastula & Ponte, 1999). Furthermore, phenomena such as El Niño Southern Oscillation (ENSO) can have some influence on the rate of dUT1 (Raut et al., 2022; Xu et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Geophysically Informed Machine Learning for Improving Rapid Estimation and Short‐Term Prediction of Earth Orientation Parameters

Kiani Shahvandi,

Dill,

Dobslaw

et al. 2023

JGR Solid Earth

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Rapid provision of Earth Orientation Parameters (EOPs, here polar motion and dUT1) is indispensable in many geodetic applications and also for spacecraft navigation. There are, however, discrepancies between the rapid EOPs and the final EOPs that have a higher latency, but the highest accuracy. To reduce these discrepancies, we focus on a data‐driven approach, present a novel method named ResLearner, and use it in the context of deep ensemble learning. Furthermore, we introduce a geophysically‐constrained approach for ResLearner. We show that the most important geophysical information to improve the rapid EOPs is the effective angular momentum functions of atmosphere, ocean, land hydrology, and sea level. In addition, semi‐diurnal, diurnal, and long‐period tides coupled with prograde and retrograde tidal excitations are important features. The influence of some climatic indices on the prediction accuracy of dUT1 is discussed and El Niño Southern Oscillation is found to be influential. We developed an operational framework, providing the improved EOPs on a daily basis with a prediction window of 63 days to fully cover the latency of final EOPs. We show that under the operational conditions and using the rapid EOPs of the International Earth Rotation and Reference Systems Service (IERS) we achieve improvements as high as 60%, thus significantly reducing the differences between rapid and final EOPs. Furthermore, we discuss how the new final series IERS 20 C04 is preferred over 14 C04. Finally, we compare against EOP hindcast experiments of the European Space Agency, on which ResLearner presents comparable improvements.

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Short-Term Polar Motion Forecast Based on the Holt-Winters Algorithm and Angular Momenta of Global Surficial Geophysical Fluids

Cited by 8 publications

References 57 publications

The Non-Seasonal Holt-Winters Method for Forecasting Stock Price Returns of Companies Affected by BDS Action

The Non-Seasonal Holt-Winters Method for Forecasting Stock Price Returns of Companies Affected by BDS Action

A new medium-long term polar motion prediction method based on sliding average within difference series

Geophysically Informed Machine Learning for Improving Rapid Estimation and Short‐Term Prediction of Earth Orientation Parameters

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