Stacked 1D Convolutional LSTM (sConvLSTM1D) Model for Effective Prediction of Sunspot Time Series

Kumar, Abhijeet; Kumar, Vipin

doi:10.1007/s11207-023-02209-3

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…In these related works, Lee 2022) used the Precursor method, and studies such as Dang et al (2022), Prasad et al (2022), X. , Kumar (2023), andSu et al (2024) employed various deep-learning techniques. Despite their differing approaches, these studies collectively forecast that Solar Cycle 25 will be stronger than Solar Cycle 24, which is in line with the latest predictions from the advanced Informer model.…”

Section: Comparison With Othersmentioning

confidence: 99%

An Improved Prediction of Solar Cycles 25 and 26 Using the Informer Model: Gnevyshev Peaks and North–South Asymmetry

Cao,

Xu,

Deng

et al. 2024

ApJ

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Forecasting the amplitude and timing of the sunspot cycle is highly important for solar physics and space weather applications, but high-precision prediction of solar magnetic activity has remained an outstanding challenge. The Informer model, as the most advanced deep learning technique, is an ideal approach for predicting solar activity cycle. Using the whole-disk sunspot numbers (SSNs) between 1749 and 2023 and the hemispheric SSNs between 1992 and 2023, the amplitudes and timings of Solar Cycles 25 and 26 are predicted by the Informer model. The main results are the following: (1) the activity levels of Solar Cycles 25 and 26 continue being weak-moderate cycles with their strengths stronger than Solar Cycle 24, implying that the long-term solar variability is significantly modulated in length and magnitude by the Gleissberg century cycle; (2) the Gnevyshev peaks of Solar Cycles 25 and 26 are clearly observed with a higher value in the second peak, suggesting that the numbers of the large sunspot groups are greater compared to the small sunspot groups in these two cycles; and (3) during Solar Cycle 25, the activity level in the southern hemisphere is predicted to be stronger than that in the northern one, revealing significant asymmetry and asynchronization between the two hemispheres. Our analysis results show that solar cycle predictions can be made more accurate if performed separately for each hemisphere. Furthermore, Solar Cycles 25 and 26 are likely to be weak-moderate cycles, in agreement with the precursor-based and model-based prediction methods.

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Section: Comparison With Othersmentioning

confidence: 99%

An Improved Prediction of Solar Cycles 25 and 26 Using the Informer Model: Gnevyshev Peaks and North–South Asymmetry

Cao,

Xu,

Deng

et al. 2024

ApJ

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“…Thus, the long short-term memory (LSTM) neural network, which was proposed by S. Hochreiter and J. Schmid Huber [31], was used to overcome insufficiencies [32]. Kumar Abhijeet and Kumar Vipin [33] presented a multi-layer deep-learning architecture for effective prediction by improving the onedimensional convolutional LSTM network with a stacked network and then forecasted the four types of sunspot numbers with different frequencies, including yearly and monthly data and 13-month smoothing. They predicted that the peak values would appear in 2024.…”

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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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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Forecast of solar cycle 25 based on Hybrid CNN-Bidirectional-GRU (CNN-BiGRU) model and Novel Gradient Residual Correction (GRC) technique

Kumar,

Kumar

2024

Advances in Space Research

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Stacked 1D Convolutional LSTM (sConvLSTM1D) Model for Effective Prediction of Sunspot Time Series

Cited by 5 publications

References 40 publications

An Improved Prediction of Solar Cycles 25 and 26 Using the Informer Model: Gnevyshev Peaks and North–South Asymmetry

An Improved Prediction of Solar Cycles 25 and 26 Using the Informer Model: Gnevyshev Peaks and North–South Asymmetry

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

Forecast of solar cycle 25 based on Hybrid CNN-Bidirectional-GRU (CNN-BiGRU) model and Novel Gradient Residual Correction (GRC) technique

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