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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Bibliographic Details

Main Authors:	Nur Haizum, Abd Rahman, Yin, Quay Pin, Hani Syahida, Zulkafli
Format:	Article
Language:	en
Published:	Universiti Kebangsaan Malaysia 2025
Subjects:	HA Statistics QA Mathematics
Online Access:	https://umpir.ump.edu.my/id/eprint/45978/1/2025%20Optimising%20LSTM%20And%20BILSTM%20Models%20For%20Time%20Series%20Forecasting%20Through%20Hyperparameter%20Tuning%20%20.pdf https://doi.org/10.17576/jqma.2103.2025.12 https://umpir.ump.edu.my/id/eprint/45978/
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