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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Kebangsaan Malaysia
2025
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/45978/ |