Applying Bayesian inference in a hybrid CNN-LSTM model for time-series prediction

Nghiem, Thi-Lich; Le, Viet-Duc; Le, Thi‐Lan; Maréchal, Pierre; Delahaye, Daniel; Vidosavljević, Andrija

doi:10.1109/mapr56351.2022.9924783

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…To verify the advantage of our method, we compare our method with some methods in other studies, including the following two categories: a single classical method (informer [38]) and hybrid methods (XGboost-DL [51] and EMD-LSTM-AM [42]). The single informer method has been proposed recently.…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

“…For example, Lee and Taesam [40] used empirical mode decomposition (EMD) and LSTM to construct a new model in order to predict the sunspot number in Solar Cycle 25. Yang and Fu et al [41] fused the attention mechanism with the EMD-LSTM model to capture abrupt points in sequence data, and their model had higher precision [42]. Nghiem and Le et al [43] presented a CNN-Bayesian LSTM method to predict time series with respect to, e.g., weather and sunspot datasets.…”

Section: Introductionmentioning

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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Section: Comparison With Other Studiesmentioning

confidence: 99%

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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“…Moreover, their proposed LSTM has 132 cells compared to our proposed LSTM with 32 cells. Nghiem et al[47] applied Bayesian inference in hybrid…”

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confidence: 99%

Novel residual hybrid machine learning for solar activity prediction in smart cities

Abdulkadir,

Hasan,

Islam

et al. 2023

Earth Sci Inform

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Predicting global solar activity is crucial for smart cities, especially space activities, communication industries, and climate change monitoring. The recently developed models to predict solar activity based on stand-alone arti cial intelligence, based on machine and deep learning models, and hybrid models are promising. Yet they may not be effective at capturing simpler linear patterns in the data and often fail to provide reliable predictions due to the computationally expensive and complex. This article proposed a novel residual hybrid machine learning method integrating linear regression machine learning, and deep learning neural networks for solving predictive accuracy in individual machine learning models that reduces complexity. The residual hybrid model leverages the capacities of the support vector machine (SVM) and long short-term memory neural network (LSTM) for hybrid SVM-LSTM model. The performance of the model is evaluated using the correlation coe cient, determination coe cient, rootmean-squared error (RMSE) and mean-absolute error. The simulation results indicated that compared to the SVM-LSTM, the training and testing RMSE of the LSTM is reduced by 76.62% and 71.18%, respectively. It also decreases the training and testing RMSE of the SVM by 77.06% and 71.81%, respectively. The proposed model can be implemented as reliable solution for accurately predicting solar activities in smart cities.

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

Kumar,

Kumar

2023

Sol Phys

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Applying Bayesian inference in a hybrid CNN-LSTM model for time-series prediction

Cited by 7 publications

References 19 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

Novel residual hybrid machine learning for solar activity prediction in smart cities

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

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