A Comprehensive Review of Swarm-Based Meta-Heuristic Algorithms for Optimizing LSTM Networks in Time Series Forecasting

Time series forecasting, a critical task in various domains, has seen significant advancements with the emergence of deep learning models like Long Short-Term Memory (LSTM) networks. These models excel at capturing temporal dependencies within data, but their performance is highly sensitive to hyperparameter optimization. Swarmbased meta-heuristic algorithms, inspired by the collective intelligence of natural systems, offer a promising approach to fine-tune LSTM architectures and parameters. This paper delves into several application of swarm-based meta-heuristic algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Grey Wolf Optimizer (GWO) to enhance LSTM-based time series forecasting. These algorithms efficiently explore vast parameter spaces, identifying optimal configurations that improve prediction accuracy. While these techniques have shown remarkable success, challenges such as computational cost issues remain. Future research should focus on addressing these limitations and developing more sophisticated hybrid approaches to further elevate the performance of LSTM networks in time series forecasting.