Unemployment Rate Prediction Using a Hybrid Model of Recurrent Neural Networks and Genetic Algorithms

Abstract

Unemployment, a significant economic and social challenge, triggers repercussions that affect individual workers and companies, generating a national economic impact. Forecasting the unemployment rate becomes essential for policymakers, allowing them to make short-term estimates, assess economic health, and make informed monetary policy decisions. This paper proposes the innovative GA-LSTM method, which fuses an LSTM neural network with a genetic algorithm to address challenges in unemployment prediction. Effective parameter determination in recurrent neural networks is crucial and a well-known challenge. The research uses the LSTM neural network to overcome complexities and nonlinearities in unemployment predictions, complementing it with a genetic algorithm to optimize the parameters. The central objective is to evaluate recurrent neural network models by comparing them with GA-LSTM to identify the most appropriate model for predicting unemployment in Ecuador using monthly data collected by various organizations. The results demonstrate that the hybrid GA-LSTM model outperforms traditional approaches, such as BiLSTM and GRU, on various performance metrics. This finding suggests that the combination of the predictive power of LSTM with the optimization capacity of the genetic algorithm offers a robust and effective solution to address the complexity of predicting unemployment in Ecuador.