Prediction Methods in Solar Sunspots Cycles

Ng, K. K.

doi:10.1038/srep21028

Cited by 11 publications

(7 citation statements)

References 33 publications

(44 reference statements)

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“…The correlation between the geomagnetic activity index and the subsequent solar activity has been explored in the past [11][12][13][14]. The current efforts are traceable to some earlier works done by Ohl [15], Thompson [16] and Feynman [17].…”

Section: Introductionmentioning

confidence: 93%

Coronal Mass Ejections, Solar Cycles and Magnetic Poles Reversal

Ng¹

2019

AJAA

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The magnitude of the measured geomagnetic index increases when the Coronal Mass Ejections occur on the Sun's surface. The abrupt increase in the geomagnetic index has seriously impacted the accuracy in the forecast of the activity of the next solar cycle. A method is proposed to filter the effect from the Coronal Mass Ejections. The correlation between the geomagnetic index and the activity of the subsequent solar cycle is found to have drastically improved with the proposed scheme. A strong correlation between the maximum amplitude R N of a solar cycle N and its pre-cycle coronal mass ejections adjusted monthly geomagnetic activity index has been qualitatively determined, as illustrated by an impressive correlation coefficient of 0.91+0.09-0.12, with its statistical significance estimated at 4.3 σ. The corrected data have significantly improved the correlation between the observed variables from their original un-corrected case of 0.63 ± 0.23. Our result indicates that the upcoming solar cycle, estimated at R 25 = 147 ± 30, would be stronger than the current waning solar cycle 24. In a related calculation, the magnetic poles reversals occurring in the solar cycles 21 and 22 are reproduced numerically from Maxwell's electromagnetic equations.

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Section: Introductionmentioning

confidence: 93%

Coronal Mass Ejections, Solar Cycles and Magnetic Poles Reversal

Ng¹

2019

AJAA

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“…Although predicting the precise behavior of a sunspot cycle is not possible, approach to a maximum/minimum with an average 5.5 year period is relatively reliable [34]. So we can use sunspot data to provide early warning for epidemiological and virological surveillance of EHF as well as global influenza outbreaks.…”

Section: Forecasting Future Pandemicsmentioning

confidence: 99%

Sunspot Activity, Influenza and Ebola Outbreak Connection

Qu¹,

Gao²

2016

Astrobiol Outreach

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An ever-present threat of a major new influenza pandemic of devastating proportions haunts us, and despite diligent monitoring of circulating viruses has been impossible to predict. On a more local scale outbreaks of Ebola Hemorrhagic Fever (EHF) in West Africa have posed a serious public health threat because of its high fatality risk. Research on the environmental factors underlying both EHF and influenza epidemiology may provide useful insights into the occurrence of future pandemic outbreaks. This study suggests that sunspot activity in extremum or ± one year was associated with influenza pandemics and severe EHF outbreaks in Africa from 1976 to 2014. Potential mechanisms by which sunspot activity may influence viral outbreaks are discussed. Current and future surveillance efforts should be supported to construct a comprehensive early warning system involving virus monitoring and epidemioilogy on the ground, sunspot activity as well as stratospheric sampling for forecasting future pandemics.

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“…The traditional methods are more suitable for the stable time series, but the sunspot a Correspondence to: Nian Fu. E-mail: funian@jiea.edu.cn *College of Electronic and Information Engineer, Beibu Gulf, Qinzhou 535011, China **College of Electronic and Information Engineer, Jiangxi Institute of Economic Administrators, Nanchang, 330088, China ***College of Resources and Environment, Beibu Gulf, Qinzhou 535011, China number is a chaotic sequence [14]. Therefore, to obtain the stable sequence, empirical mode decomposition (EMD) [15,16] or a variational mode decomposition (VMD) [17], generally combined with other prediction methods, are used to decompose the chaotic data [18,19].…”

Section: Introductionmentioning

confidence: 99%

The Sunspot Number Forecasting Using a Hybridization Model of EMD, LSTM and Attention Mechanism

Yang,

Fu,

Chen

2023

IEEJ Transactions Elec Engng

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Sunspot number forecasting is a significant task for human beings in order to observe solar activity, and it is a classical chaotic time series. To improve the forecasting accuracy, we proposed a hybrid forecasting model based on empirical mode decomposition, long short‐term memory neural network and attention mechanism. First, the empirical mode decomposition is used to transfer the sunspot number to several sub‐components called IMFs and residual. Then, the IMFs and residual are input to the prediction module that is composed of long short‐term memory neural network and attention mechanism. Finally, the forecasting result is accumulated by the IMFs and residual through the predict module. The RMSE, MAE, MAPE and R2 indexes of our method are 1.567, 1.048, 1.8% and 0.9997. We also compare with GRU, informer and XGBoost‐DL, our method improves accuracy above 24 and 18 of RMSE and MAE, respectively. The experiment results demonstrate our method has higher precision. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Prediction Methods in Solar Sunspots Cycles

Cited by 11 publications

References 33 publications

Coronal Mass Ejections, Solar Cycles and Magnetic Poles Reversal

Coronal Mass Ejections, Solar Cycles and Magnetic Poles Reversal

Sunspot Activity, Influenza and Ebola Outbreak Connection

The Sunspot Number Forecasting Using a Hybridization Model of EMD, LSTM and Attention Mechanism

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