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Oct 22, 2024 , In this paper, a forecasting method is proposed, which has a strong capability of predicting highly fluctuating demand data.
5 days ago , (2) CNNA-BiLSTM model is developed by integrating convolutional neural network (CNN) and self-attention mechanism (SAM) modules into the bi-directional long ...
Oct 22, 2024 , This paper proposes a novel forecasting method that combines the deep learning method – long short-term memory (LSTM) networks and random forest (RF).
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7 days ago , A Bi-LSTM network integrates two hidden layers that process data in opposite directions before merging their outputs into a single layer.
Oct 24, 2024 , Deep learning models such as Long Short-Term Memory Networks (LSTMs) are essential for tackling time series data analysis and prediction challenges. In ...
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Oct 25, 2024 , The Bidirectional Long Short-Term Memory Network is a variant of RNN network. Through processing the input sequence in two directions, forward and backward, ...
4 days ago , In this paper, we propose the design of an LSTM network to forecast the sales of a lumber mill, considering two scenarios: (1) costs of product construction ...
Oct 10, 2024 , This paper introduces a TSK-type-based self-evolving compensatory interval type-2 fuzzy Long short-term memory (LSTM) neural network (TSECIT2FNN-LSTM) soft ...
Oct 14, 2024 , This model features a two-layer architecture. The lower layer employs a long short-term memory network (LSTM) to capture short-term temporal correlations within.
7 days ago , The objective of the presented systematic literature review is hence to provide a comprehensive overview of the various modeling approaches and application ...