First, VMD was adopted to decompose the water quality data into a series of relatively stable components, with the aim to reduce the instability of the original data and increase the predictability, then each component was input into the IGOA-LSTM model for prediction. Finally, each component ...
The hybrid models perform significantly better than the single models, and the forecasting accuracy of the VMD-based models is generally higher than that of the EMD-based models. 展开 关键词: Decomposition-and-ensemble VMD LSTM Stock price forecasting Hybrid model ...
Nascimento EGS, de Melo TA, Moreira DM (2023) A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy. Energy 278:127678 Article Google Scholar Parri S, Teeparthi K (2024) Vmd-scinet: a hybrid model for improved wind speed forecasting. Eart...
The hybrid model was proved to be better than single LSTM and hybrid WT-LSTM. Neshat et al. [36] proposed a quaternion convolutional long short-term memory neural model based on VMD, BiLSTM and CNN. The result shows that this hybrid model outperformed other benchmark models s in terms of...
In this study, we present the EEG-GCN, a novel hybrid model for the prediction of time series data, adept at addressing the inherent challenges posed by the data's complex, non-linear, and periodic nature, as well as the noise that frequently accompanies it. This model synergizes signal de...
For example, a hybrid approach combining variational mode decomposition (VMD), whale optimization algorithm (WOA), and LSTM has been proposed to accurately predict photovoltaic power, demonstrating improved predictive performance for stable electric power variations and large fluctuations [17]. Another ...
The results show that compared with other benchmark models, the VMD-SGMD-LSTM hybrid model proposed in this paper has better forecasting ability and robustness for different agricultural futures, which effectively makes up for the shortcomings of existing research.Frontiers in Sustainable Food Systems...
Motor Imagery A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals LSTM Computers in Biology and Medicine 2022 Motor Imagery An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-mac...
To improve the accuracy of stock price index prediction, this paper introduces a new hybrid model, VMD-LSTM, that combines variational mode decomposition (VMD) and a long short-term memory (LSTM) network. The proposed model is based on decomposition-and-ensemble framework. VMD is a data-...
and high fitting capabilities and it slightly surpasses the others models. Thirdly, air temperature was decomposed into several intrinsic mode functions (IMF) using the VMD method and the performances of the models were evaluated. The VMD parameters appeared to cause much influence on the prediction...