Model estimation and prediction of sunspots cycles through AR-GARCH models

Zaffar, Asma; Abbas, Shujaat; Ansari, Muhammad Rashid

doi:10.1007/s12648-021-02135-9

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Within the realm of sunspot prediction, diverse methodologies have been employed to address forecasting challenges, broadly categorized into linear and nonlinear modeling approaches 13 – 15 . However, the time series data representing sunspot numbers exhibits distinctive features, such as uncertainty, volatility, and cyclicity 16 , 17 . Hence, nonlinear modeling techniques prove more suitable for sunspot number forecasting.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss

Cui,

Ding,

et al. 2024

Sci Rep

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Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a seasonal-trend decomposition procedure based on loess to tackle challenges related to sequence prediction, such as self-correlations and non-stationarity. To address prediction uncertainty, we replace the traditional $$l_2$$ l 2 -norm loss with pinball loss. This modification extends the conventional GRU-based point forecasting to a probabilistic framework expressed as quantiles. We apply our proposed model to analyze a well-established historical sunspot dataset for both single- and multi-step ahead forecasting. The results demonstrate the effectiveness of our combined model in predicting sunspot values, surpassing the performance of other existing methods.

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Section: Introductionmentioning

confidence: 99%

Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss

Cui,

Ding,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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“…Reference [23] improved the CNN with self‐learning and the performance is tested under different types of time series data. Reference [24] combined the statistical generalized autoregressive conditional heteroskedasticity model (GARCH) and AR to forecast SSN. Reference [25] proposed a deep fuzzy long short‐term memory architecture.…”

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 of Sunspot Number with Hybrid Model Based on 1D-CNN, BiLSTM and Multi-Head Attention Mechanism

Chen,

Liu,

Yang

et al. 2024

Electronics

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Sunspots have a significant impact on human activities. In this study, we aimed to improve solar activity prediction accuracy. To predict the sunspot number based on different aspects, such as extracted features and relationships among data, we developed a hybrid model that includes a one-dimensional convolutional neural network (1D-CNN) for extracting the features of sunspots and bidirectional long short-term memory (BiLSTM) embedded with a multi-head attention mechanism (MHAM) to learn the inner relationships among data and finally predict the sunspot number. We evaluated our model and several existing models according to different evaluation indicators, such as mean absolute error (MAE) and root mean square error (RMSE). Compared with the informer, stacked LSTM, XGBoost-DL, and EMD-LSTM-AM models, the RMSE and MAE of our results were more than 42.5% and 65.1% lower, respectively. The experimental results demonstrate that our model has higher accuracy than other methods.

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Model estimation and prediction of sunspots cycles through AR-GARCH models

Cited by 3 publications

References 10 publications

Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss

Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss

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

Prediction of Sunspot Number with Hybrid Model Based on 1D-CNN, BiLSTM and Multi-Head Attention Mechanism

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