Optimising LSTM and BILSTM models for time series forecasting through hyperparameter tuning
Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) are the emerging Recurrent Neural Networks (RNN) widely used in time series forecasting. The performance of these neural networks relies on the selection of hyperparameters. A random selection of the hyperparameters may...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2025
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| Online Access: | http://journalarticle.ukm.my/26426/1/Paper_12%20-.pdf http://journalarticle.ukm.my/26426/ https://www.ukm.my/jqma/ |
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