History of Sunspot Research and Forecast of the Maximum of Solar Cycle 25

Vasiljeva, I. E.; Pishkalo, M. І.

doi:10.3103/s0884591321040073

Cited by 6 publications

(5 citation statements)

References 67 publications

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“…After the gating signal is computed, the scope of h(t) ca cording to Formula (8): where Wz is a weight matrix of the update gate. Then, h(t) can be updated using Formula (10):…”

Section: Grumentioning

confidence: 99%

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A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers

Yang,

Liu,

Xuan

et al. 2024

Electronics

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To improve the predictive accuracy of sunspot numbers, a hybrid model was built to forecast future sunspot numbers. In this paper, we present a prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU), and error compensation for predicting sunspot numbers. CEEMAND is applied to decompose the original sunspot number data into several components, which are then used to train and test the GRU for the optimal parameters of the corresponding sub-models. Error compensation is utilized to solve the delay phenomenon between the original sunspot number and the predictive result. We compare our method with the informer, extreme gradient boosting combined with deep learning (XGboost-DL), and empirical mode decomposition combined long short-term memory neutral network and attention mechanism (EMD-LSTM-AM) methods, and evaluation metrics, such as RMSE and MAE, are used to measure their performance. Our method decreases more than 2.2813 and 3.5827 relative to RMSE and MAE, respectively. Thus, the experiment can demonstrate that our method has an obvious advantage compared to others.

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“…After the gating signal is computed, the scope of h(t) ca cording to Formula (8): where Wz is a weight matrix of the update gate. Then, h(t) can be updated using Formula (10):…”

Section: Grumentioning

confidence: 99%

“…In recent years, statistical models have commonly been applied to predict the sunspot number, such as exponential smoothing [7,8], Kalman filters [9], autoregressive moving average [10], etc. The autoregressive moving average (ARMA) is a well-known classical statistical method [11].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers

Yang,

Liu,

Xuan

et al. 2024

Electronics

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“…1 º ïî ïå ðåä -í³ìè. Ôà çó Ð ìè ðîç ðà õó âà ëè ó ïðè ïó ùåíí³, ùî òðè âàë³ñòü 25-ãî öèê ëó â³äïîâ³äàº ñå ðåäí³é òðè âà ëîñò³ ñî íÿ÷ íî ãî öèê ëó (11.019 ð., çã³äíî ³ç ïà ðà ìåò ðà ìè öèêë³â ñî íÿ÷ íî¿ àê òèâ íîñò³ çà âåðñ³ºþ 2.0 [23]). Äëÿ ðîçðà õóí êó ïà ðà ìåò ðà Ô ïî ïå ðåä íüî íà ìè áó ëî çíàé äå íî ïðî ãíî çî âà íó òðè âàë³ñòü ôà çè ðîñ òó àê òèâ íîñò³ ó 25-ìó öèêë³ (4.254 ð.)…”

Section: T T T T T T T Riseunclassified

Photometric flattening index of the solar corona in the solar cycle

Pishkalo

2023

Kinemat. fiz. nebesnyh tel (Online)

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²íäåêñ ôî òî ìåò ðè÷ íî¿ ñòèñíó òîñò³ ñî íÿ÷ íî¿ êî ðî íè ó öèêë³ ñî íÿ÷ íî¿ àê òèâ íîñò³ ²íäåêñ ôî òî ìåò ðè÷ íî¿ ñòèñ íó òîñò³ ÿê ê³ëüê³ñíó õà ðàê òå ðèñ òè êó ôîð ìè ñî íÿ÷ íî¿ êî ðî íè, ñïîñ òå ðå aeå íî¿ ï³ä ÷àñ ïî âíî ãî ñî íÿ÷ íî ãî çàòåì íåí íÿ, áóëî çà ïðî ïî íî âà íî Ëþ äåí äîð ôîì ó 1930-õ ðð. Ó ðî áîò³ ç³áðà íî çíà ÷åí íÿ ³íäåê ñó ñòèñ íó òîñò³ äëÿ 69 çà òåì íåíü 1851… 2020 ðð. ³ äîñë³äaeå íî ¿õí³é çâ'ÿ çîê ³ç ïà ðà ìåò ðà ìè öèê ëó ñî íÿ÷ íî¿ àêòèâ íîñò³. Çíà ÷åí íÿ ³íäåê ñó ñòèñ íó òîñò³ çì³íþºòüñÿ ïðè áëèç íî â³ä 0.3…0.4 ó ì³í³ìóì³ öèê ëó àê òèâ íîñò³ äî 0.0…0.1 -ó ìàê ñè ìóì³. ²íäåêñ êî ðå ëþº ³ç â³äíîñ íèì ÷èñ ëîì ñî íÿ÷ íèõ ïëÿì ³ ôà çîþ ñî íÿ÷ íî ãî öèê ëó. Çíà ÷åí íÿ êîåô³ö³ºíòà êî ðå ëÿö³¿ ì³ae ³íäåê ñîì ñòèñ íó òîñò³ ³ äî áî âèì, ì³ñÿ÷ íèì ³ ì³ñÿ÷ íèì çãëàä aeå íèì ÷èñ ëîì ñî íÿ÷ íèõ ïëÿì ñòàíî âèòü â³äïîâ³äíî -0.577 (ð < 4×10 -7 ), -0.595 (p < 8×10 -8 ) ³ -0.598 (p < < 7×10 -8 ), à ì³ae ³íäåê ñîì ñòèñ íó òîñò³ ³ ôà çîþ öèê ëó Ô äëÿ ïåð³îäó ðîñ òó ³ ñïà äó ñî íÿ÷ íî ãî öèê ëó -â³äïîâ³äíî -0.759 (p < 5×10 -6 ) ³ 0.660 (p < 2×10 -6 ). Ñïîñ òå ðå aeå íà ôîð ìà ñî íÿ÷ íî¿ êî ðî íè, çîê ðå ìà ³ âå ëè ÷èíà ³íäåê ñó ñòèñ íó òîñò³, âèç íà ÷àºòüñÿ ãëî áàëü íèì ìàãí³òíèì ïî ëåì Ñîí öÿ, ïå ðå âàae íî éîãî äè ïîëü íîþ ñêëàäîâîþ. Êëþ ÷îâ³ ñëî âà: Ñîí öå, ñî íÿ÷ íà êî ðî íà, öèêë ñî íÿ÷ íî¿ àê òèâ íîñò³ ÂÑÒÓÏ Ôîð ìà, ñòðóê òó ðà ³ ÿñ êðàâ³ñòü ñî íÿ÷ íî¿ êî ðî íè, ñïîñ òå ðå aeå íî¿ ï³ä ÷àñ ïî âíèõ ñî íÿ÷ íèõ çà òåì íåíü, çì³íþºòüñÿ â³ä çà òåì íåí íÿ äî çà òåì íåííÿ. Áà ãà òîð³÷í³ ñïîñ òå ðå aeåí íÿ ïî êà çó þòü, ùî ó öèõ çì³íàõ º ïåâí³ çàêî íîì³ðíîñò³. Çîê ðå ìà, ïî áëè çó ì³í³ìóìó öèê ëó ñî íÿ÷ íî¿ àê òèâ íîñò³ êî ðî íà âè òÿã íó òà âçäîâae åê âà òî ðà âíàñë³äîê íà ÿâ íîñò³ äâîõ ÿñ êðà âèõ

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“…solar maximum) appears to be foreseeable, the cycle fluctuates in magnitude and duration. Estimating the predicted influence of solar activity on space missions and social technology requires an accurate sunspot number (SSN) forecast [8]. Many facets of human health are thought to be influenced by solar cycles [2].…”

Section: Introductionmentioning

confidence: 99%

“…The quasi-periodic nature of the solar cycle makes it an excellent candidate for applying time-series forecasting methods such as statistical and machine learning techniques to the sunspot number dataset [8]. Time-series forecasting uses statistical and machine-learning algorithms to evaluate historical data in an attempt to predict future values.…”

Section: Introductionmentioning

confidence: 99%

Comparison of different predictive models and their effectiveness in sunspot number prediction

Moustafa

Khodairy

2023

Phys. Scr.

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Time series forecasting is one of the most critical challenges in statistics anddata science. Human activities and health are significantly influenced by solar activity. Thesunspot number is one of the most commonly used measures of solar activity. The solarcycle’s quasi-periodic nature makes it an excellent choice for time series forecasting. Fourmodels include three singular models, consisting of LSTM, ARIMA, and SARIMA, as wellas a hybrid model were implemented to forecast maximum sunspot number of cycles 25 and26. The hyperparameters of the singular models were optimized using Bayesian optimization.The LSTM-ARIMA hybrid model was able to achieve the best performance. The outstandingresults of the LSTM-ARIMA model shows the potential of hybrid methods in improving theoverall performance. Moreover, the LSTM model was able to outperform the ARIMA model,which demonstrates the ability of LSTM networks in learning from time-series data. The finalmodel forecasts a peak sunspot number of 137.04 for Solar Cycle 25 in September 2024 and164.3 for Solar Cycle 26 in December 2034, which is comparable to NASA’s prediction of134.4 in October 2024 and 161.2 in December 2034.

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History of Sunspot Research and Forecast of the Maximum of Solar Cycle 25

Cited by 6 publications

References 67 publications

A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers

A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers

Photometric flattening index of the solar corona in the solar cycle

Comparison of different predictive models and their effectiveness in sunspot number prediction

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