Sunspot Number Prediction Using Gated Recurrent Unit (GRU) Algorithm

Arfianti, Unix Izyah; Novitasari, Dian Candra Rini; Widodo, Nanang; Hafiyusholeh, Moh.; Utami, Wika Dianita

doi:10.22146/ijccs.63676

Cited by 11 publications

(4 citation statements)

References 24 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reference [34] adopted the two methods to forecast the SSN from 2000 to 2020. Reference [46] used the GRU algorithm in the deep learning algorithm to predict and analyze the sunspot sequence, and achieved better results than classical algorithms such as ARIMA and Naive. The experimental result (Table VI) shows RMSE and MAE of informer and GRU method are 29.9 and 22.35, 37.14 and 26.77.…”

Section: Experimental Analysis and Discussionmentioning

confidence: 99%

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

Yang,

Fu,

Chen

2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

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.

show abstract

Section: Experimental Analysis and Discussionmentioning

confidence: 99%

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

Yang,

Fu,

Chen

2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…) and reset ( ), that modulate the information flow from the previous time step to the current step. At each time step , the update gate decides the amount of previous information that should be retained, and the reset gate determines the amount of information that needs to be forgotten [53]. The GRU hidden state at the time is defined by the following formulae [54]:…”

Section: ) Temporal Information Extraction-based Attention Mechanismmentioning

confidence: 99%

A Novel Smart Deepfake Video Detection System

Elpeltagy¹,

Ismail²,

Zaki³

et al. 2023

IJACSA

View full text Add to dashboard Cite

Rapid advancements in deep learning-based technologies have developed several synthetic video and audio generation methods producing incredibly hyper-realistic deepfakes. These deepfakes can be employed to impersonate the identity of a source person in videos by swapping the source's face with the target one. Deepfakes can also be used to clone the voice of a target person utilizing audio samples. Such deepfakes may pose a threat to societies if they are utilized maliciously. Consequently, distinguishing either one or both deepfake visual video frames and cloned voices from genuine ones has become an urgent issue. This work presents a novel smart deepfake video detection system. The video frames and audio are extracted from given videos. Two feature extraction methods are proposed, one for each modality; visual video frames, and audio. The first method is an upgraded XceptionNet model, which is utilized for extracting spatial features from video frames. It produces feature representation for visual video frames. The second one is a modified InceptionResNetV2 model based on the Constant-Q Transform (CQT) method. It is employed to extract deep timefrequency features from the audio modality. It produces feature representation for the audio. The corresponding extracted features of both modalities are fused at a mid-layer to produce a bimodal information-based feature representation for the whole video. These three representation levels are independently fed into the Gated Recurrent Unit (GRU) based attention mechanism helping to learn and extract deep and important temporal information per level. Then, the system checks whether the forgery is only applied to video frames, audio, or both, and produces the final decision about video authenticity. The newly suggested method has been evaluated on the FakeAVCeleb multimodal videos dataset. The experimental results analysis assures the superiority of the new method over the current-stateof-the-art methods.

show abstract

“…Panigrahi and Pattanayak et al [47] applied the hybridization of the ARIMA, exponential smoothing, and SVM to predict sunspot number time series. Dai and Liu et al [48] adopted the phase space reconstruction method to transform the form of the sunspot dataset for adapting the neural network; then, the reconstructed data were input into a temporal convolutional neural (TCN) network. They predicted the sunspot number from January 2020 to December 2030.…”

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

View full text Add to dashboard Cite

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.

show abstract

Sunspot Number Prediction Using Gated Recurrent Unit (GRU) Algorithm

Cited by 11 publications

References 24 publications

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

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

A Novel Smart Deepfake Video Detection System

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

Contact Info

Product

Resources

About