Optimising LSTM and BiLSTM models for time series forecasting through hyperparameter tuning

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:English
Published: Universiti Kebangsaan Malaysia 2025
Subjects:
HA Statistics
QA Mathematics
Online Access:https://umpir.ump.edu.my/id/eprint/45978/

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