Enhancing Neural Network with Particle Swarm: A House Price Prediction Case

Abstract

This study delves into the utilization of the Particle Swarm Optimization (PSO) algorithm to discover the best parameters of Long Short-Term Memory (LSTM) model with the aim of housing price prediction. Employing the Ames Housing dataset, our investigation underscores the remarkable efficacy of PSO in mitigating prediction errors within the LSTM framework, thereby enhancing overall predictive performance. The symbiotic integration of PSO with LSTM facilitates the discernment of intricate data patterns, particularly in scenarios involving high-dimensional and nonlinear optimization conundrums. The findings illuminate PSO's ability to expediently traverse and identify global optima. The outcomes unequivocally underscore the PSO-LSTM model's superior attributes, characterized by accelerated convergence, diminished errors, and exceptional proficiency. In summation, this investigation underscores PSO's promise as a potent technique for optimizing machine learning models, notably exemplified by its integration with the LSTM framework.