Optimizing bidirectional long short-term memory networks for univariate time series forecasting: a comprehensive guide
Mostafa Salaheldin Abdelsalam Abotaleb, Pushan Kumar Dutta · 2024
This chapter undertakes an in-depth examination of refining bidirectional long short-term memory (BiLSTM) networks for univariate time series forecasting, a pivotal segment within predictive analytics that finds relevance across diverse sectors such as finance, healthcare, and energy. Despite the prevalent use of BiLSTM models in forecasting multivariate time series, tailoring these models for univariate data - which consists of observations of a single time-dependent variable - introduces distinct challenges and opportunities. The discussion commences with a detailed introduction to BiLSTM networks, highlighting the unique aspects of their architecture that render them exceptionally capable of identifying long-term dependencies within time series data. Subsequently, the narrative explores the strategies for customizing BiLSTMs for univariate forecasting endeavors, covering aspects such as data preprocessing, adjustments in network structure, fine-tuning of hyperparameters, and the application of regularization methods to bolster model accuracy and mitigate the risk of overfitting. A notable contribution of this study is the development of a comprehensive methodology for the deployment and critical assessment of BiLSTM models, supported by an empirical investigation using authentic datasets to confirm the effectiveness of the suggested modifications. Comparative analyses demonstrate the enhanced performance of finely tuned BiLSTM models over conventional forecasting techniques and basic neural network models. This chapter is designed to act as an authoritative resource for both practitioners and scholars aiming to exploit the advanced predictive capabilities of BiLSTMs for univariate time series prediction, offering practical guidance for the meticulous crafting, evaluation, and application of these models.