Predicting Unemployment Rates with Modified Metaheuristic Optimized Echo State Networks

Dejan Bulaja, Lepa Babic, Vico Zeljkovic, Aleksandar Djordjevic, Miodrag Zivkovic, Milos Antonijevic, Vladimir Marevic and Nebojsa Bacanin ()
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Dejan Bulaja: Singidunum University
Lepa Babic: Singidunum University
Vico Zeljkovic: Singidunum University
Aleksandar Djordjevic: Singidunum University
Miodrag Zivkovic: Singidunum University
Milos Antonijevic: Singidunum University
Vladimir Marevic: Singidunum University
Nebojsa Bacanin: Singidunum University

Chapter Chapter 14 in Global Investment Decisions in the Circular Economy, 2025, pp 185-199 from Springer

Abstract: Abstract Unemployment is a critical factor in the global economy, influenced by both economic and non-economic variables. The complexity of trends and fluctuations makes forecasting unemployment rates challenging, hindering policymakers in implementing effective measures to mitigate economic impact. Traditional forecasting methods often struggle with capturing non-linear dependencies and sudden shifts in labor markets. This work explores the use of echo state networks for unemployment forecasting based on publicly available historical economic data. A modified optimizer is proposed to address the challenging task of hyperparameter selection in echo state networks, ensuring favorable performance and improved generalization. Evaluations on real-world data demonstrate promising results, with best generated model achieving a low mean squared error of 0.006788, highlighting the potential of reservoir computing in enhancing predictive accuracy and supporting data-driven economic decision-making.

Keywords: Unemployment; Prediction; Echo state networks; Metaheuristics optimization; Artificial bee colony; Stochastic optimizers (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-3-031-86236-6_14

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DOI: 10.1007/978-3-031-86236-6_14

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