Unemployment rate forecasting: LSTM-GRU hybrid approach

Mustafa Yurtsever ()
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Mustafa Yurtsever: Dokuz Eylul University

Journal for Labour Market Research, 2023, vol. 57, issue 1, 1-9

Abstract: Abstract Unemployment rates provide information on the economic development of countries. Unemployment is not only an economic problem but also a social one. As such, unemployment rates are important for governments and policy makers. Therefore, researchers around the world are constantly developing new forecasting models to successfully predict the unemployment rate. This article presents a new model that combines two deep learning methodologies used for time series forecasting to find the future state of the unemployment rate. The model, created by combining LSTM and GRU layers, has been used to forecast unemployment rates in the United States, United Kingdom, France and Italy. Monthly unemployment rate data was used as the dataset between January 1983 and May 2022. The model’s performance was evaluated using RMSE, MAPE, and MAE values and compared to a stand-alone LSTM and GRU model. Results indicate that the hybrid model performed better for the four countries, except for Italy where the GRU model yielded better results.

Keywords: Unemployment; Forecasting; Deep learning (search for similar items in EconPapers)
JEL-codes: C51 C52 C53 E24 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1186/s12651-023-00345-8

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