2023
DOI: 10.3934/mbe.2023739
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Deep belief improved bidirectional LSTM for multivariate time series forecasting

Keruo Jiang,
Zhen Huang,
Xinyan Zhou
et al.

Abstract: <abstract> <p>Multivariate time series (MTS) play essential roles in daily life because most real-world time series datasets are multivariate and rich in time-dependent information. Traditional forecasting methods for MTS are time-consuming and filled with complicated limitations. One efficient method being explored within the dynamical systems is the extended short-term memory networks (LSTMs). However, existing MTS models only partially use the hidden spatial relationship as effectively as LSTMs.… Show more

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Cited by 3 publications
(2 citation statements)
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“…To detect the presence of the intrusions, the ABiLSTM model is applied. LSTM is a revised edition of the classical RNN that exploits the specially adapted memory units to effectively express the long-term dependency of the MTS dataset [20]. The LSTM model's design provides an effective solution to the gradient disappearing problem on the contrary to the traditional RNN methods.…”
Section: Intrusion Detection Using Abilstm Modelmentioning
confidence: 99%
“…To detect the presence of the intrusions, the ABiLSTM model is applied. LSTM is a revised edition of the classical RNN that exploits the specially adapted memory units to effectively express the long-term dependency of the MTS dataset [20]. The LSTM model's design provides an effective solution to the gradient disappearing problem on the contrary to the traditional RNN methods.…”
Section: Intrusion Detection Using Abilstm Modelmentioning
confidence: 99%
“…For breast mass classification, the DBN approach can be utilized. The DBN model efficiently leverages the residuals based on the predicted values of the MLP model and the corresponding actual observation to enable prediction [26]. The preliminary step of this component includes the visible layer (VL) of RBM receiving the input datasets-viz., the difference between the observed and predicted values of MLP models.…”
Section: Image Classification Using the Dbn Modelmentioning
confidence: 99%