Unemployment rate forecasting : LSTM-GRU hybrid approach

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 fore...
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Autor*in:	Yurtsever, Mustafa [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Rechteinformationen:	Open Access Namensnennung 4.0 International ; CC BY 4.0

Schlagwörter:	Unemployment Forecasting Deep learning

Übergeordnetes Werk:	Enthalten in: Journal for labour market research - Berlin : Springer, 2004, 57(2023), 1 vom: Dez., Artikel-ID 18, Seite 1-9
Übergeordnetes Werk:	volume:57 ; year:2023 ; number:1 ; month:12 ; elocationid:18 ; pages:1-9

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.1186/s12651-023-00345-8

Katalog-ID:	1860573541

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allfields	10.1186/s12651-023-00345-8 doi 10419/278079 hdl (DE-627)1860573541 (DE-599)KXP1860573541 DE-627 ger DE-627 rda eng Yurtsever, Mustafa verfasserin aut Unemployment rate forecasting LSTM-GRU hybrid approach Mustafa Yurtsever 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 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. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Unemployment (dpeaa)DE-206 Forecasting (dpeaa)DE-206 Deep learning (dpeaa)DE-206 Enthalten in Journal for labour market research Berlin : Springer, 2004 57(2023), 1 vom: Dez., Artikel-ID 18, Seite 1-9 Online-Ressource (DE-627)535186827 (DE-600)2375725-5 (DE-576)267378165 2510-5027 nnns volume:57 year:2023 number:1 month:12 elocationid:18 pages:1-9 https://link.springer.com/content/pdf/10.1186/s12651-023-00345-8.pdf Verlag kostenfrei https://doi.org/10.1186/s12651-023-00345-8 Resolving-System kostenfrei https://hdl.handle.net/10419/278079 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_183 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2037 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2119 GBV_ILN_2153 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 57 2023 1 12 18 1-9 26 01 0206 4380507351 x1z 02-10-23 2403 01 DE-LFER 438336831X 00 --%%-- --%%-- n --%%-- l01 10-10-23 2403 01 DE-LFER https://doi.org/10.1186/s12651-023-00345-8 2403 01 DE-LFER https://link.springer.com/content/pdf/10.1186/s12651-023-00345-8.pdf
spelling	10.1186/s12651-023-00345-8 doi 10419/278079 hdl (DE-627)1860573541 (DE-599)KXP1860573541 DE-627 ger DE-627 rda eng Yurtsever, Mustafa verfasserin aut Unemployment rate forecasting LSTM-GRU hybrid approach Mustafa Yurtsever 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 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. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Unemployment (dpeaa)DE-206 Forecasting (dpeaa)DE-206 Deep learning (dpeaa)DE-206 Enthalten in Journal for labour market research Berlin : Springer, 2004 57(2023), 1 vom: Dez., Artikel-ID 18, Seite 1-9 Online-Ressource (DE-627)535186827 (DE-600)2375725-5 (DE-576)267378165 2510-5027 nnns volume:57 year:2023 number:1 month:12 elocationid:18 pages:1-9 https://link.springer.com/content/pdf/10.1186/s12651-023-00345-8.pdf Verlag kostenfrei https://doi.org/10.1186/s12651-023-00345-8 Resolving-System kostenfrei https://hdl.handle.net/10419/278079 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_183 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2037 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2119 GBV_ILN_2153 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 57 2023 1 12 18 1-9 26 01 0206 4380507351 x1z 02-10-23 2403 01 DE-LFER 438336831X 00 --%%-- --%%-- n --%%-- l01 10-10-23 2403 01 DE-LFER https://doi.org/10.1186/s12651-023-00345-8 2403 01 DE-LFER https://link.springer.com/content/pdf/10.1186/s12651-023-00345-8.pdf
allfields_unstemmed	10.1186/s12651-023-00345-8 doi 10419/278079 hdl (DE-627)1860573541 (DE-599)KXP1860573541 DE-627 ger DE-627 rda eng Yurtsever, Mustafa verfasserin aut Unemployment rate forecasting LSTM-GRU hybrid approach Mustafa Yurtsever 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 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. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Unemployment (dpeaa)DE-206 Forecasting (dpeaa)DE-206 Deep learning (dpeaa)DE-206 Enthalten in Journal for labour market research Berlin : Springer, 2004 57(2023), 1 vom: Dez., Artikel-ID 18, Seite 1-9 Online-Ressource (DE-627)535186827 (DE-600)2375725-5 (DE-576)267378165 2510-5027 nnns volume:57 year:2023 number:1 month:12 elocationid:18 pages:1-9 https://link.springer.com/content/pdf/10.1186/s12651-023-00345-8.pdf Verlag kostenfrei https://doi.org/10.1186/s12651-023-00345-8 Resolving-System kostenfrei https://hdl.handle.net/10419/278079 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_183 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2037 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2119 GBV_ILN_2153 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 57 2023 1 12 18 1-9 26 01 0206 4380507351 x1z 02-10-23 2403 01 DE-LFER 438336831X 00 --%%-- --%%-- n --%%-- l01 10-10-23 2403 01 DE-LFER https://doi.org/10.1186/s12651-023-00345-8 2403 01 DE-LFER https://link.springer.com/content/pdf/10.1186/s12651-023-00345-8.pdf
allfieldsGer	10.1186/s12651-023-00345-8 doi 10419/278079 hdl (DE-627)1860573541 (DE-599)KXP1860573541 DE-627 ger DE-627 rda eng Yurtsever, Mustafa verfasserin aut Unemployment rate forecasting LSTM-GRU hybrid approach Mustafa Yurtsever 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 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. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Unemployment (dpeaa)DE-206 Forecasting (dpeaa)DE-206 Deep learning (dpeaa)DE-206 Enthalten in Journal for labour market research Berlin : Springer, 2004 57(2023), 1 vom: Dez., Artikel-ID 18, Seite 1-9 Online-Ressource (DE-627)535186827 (DE-600)2375725-5 (DE-576)267378165 2510-5027 nnns volume:57 year:2023 number:1 month:12 elocationid:18 pages:1-9 https://link.springer.com/content/pdf/10.1186/s12651-023-00345-8.pdf Verlag kostenfrei https://doi.org/10.1186/s12651-023-00345-8 Resolving-System kostenfrei https://hdl.handle.net/10419/278079 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_183 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2037 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2119 GBV_ILN_2153 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 57 2023 1 12 18 1-9 26 01 0206 4380507351 x1z 02-10-23 2403 01 DE-LFER 438336831X 00 --%%-- --%%-- n --%%-- l01 10-10-23 2403 01 DE-LFER https://doi.org/10.1186/s12651-023-00345-8 2403 01 DE-LFER https://link.springer.com/content/pdf/10.1186/s12651-023-00345-8.pdf
allfieldsSound	10.1186/s12651-023-00345-8 doi 10419/278079 hdl (DE-627)1860573541 (DE-599)KXP1860573541 DE-627 ger DE-627 rda eng Yurtsever, Mustafa verfasserin aut Unemployment rate forecasting LSTM-GRU hybrid approach Mustafa Yurtsever 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 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. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Unemployment (dpeaa)DE-206 Forecasting (dpeaa)DE-206 Deep learning (dpeaa)DE-206 Enthalten in Journal for labour market research Berlin : Springer, 2004 57(2023), 1 vom: Dez., Artikel-ID 18, Seite 1-9 Online-Ressource (DE-627)535186827 (DE-600)2375725-5 (DE-576)267378165 2510-5027 nnns volume:57 year:2023 number:1 month:12 elocationid:18 pages:1-9 https://link.springer.com/content/pdf/10.1186/s12651-023-00345-8.pdf Verlag kostenfrei https://doi.org/10.1186/s12651-023-00345-8 Resolving-System kostenfrei https://hdl.handle.net/10419/278079 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_183 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2037 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2119 GBV_ILN_2153 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 57 2023 1 12 18 1-9 26 01 0206 4380507351 x1z 02-10-23 2403 01 DE-LFER 438336831X 00 --%%-- --%%-- n --%%-- l01 10-10-23 2403 01 DE-LFER https://doi.org/10.1186/s12651-023-00345-8 2403 01 DE-LFER https://link.springer.com/content/pdf/10.1186/s12651-023-00345-8.pdf
language	English
source	Enthalten in Journal for labour market research 57(2023), 1 vom: Dez., Artikel-ID 18, Seite 1-9 volume:57 year:2023 number:1 month:12 elocationid:18 pages:1-9
sourceStr	Enthalten in Journal for labour market research 57(2023), 1 vom: Dez., Artikel-ID 18, Seite 1-9 volume:57 year:2023 number:1 month:12 elocationid:18 pages:1-9
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topic_facet	Unemployment Forecasting Deep learning
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publishDateDaySort_date	2023-12-01T00:00:00Z
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topic_title	Unemployment rate forecasting LSTM-GRU hybrid approach Mustafa Yurtsever Unemployment (dpeaa)DE-206 Forecasting (dpeaa)DE-206 Deep learning (dpeaa)DE-206
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title	Unemployment rate forecasting LSTM-GRU hybrid approach
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title_full	Unemployment rate forecasting LSTM-GRU hybrid approach Mustafa Yurtsever
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title_auth	Unemployment rate forecasting LSTM-GRU hybrid approach
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.
abstractGer	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.
abstract_unstemmed	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.
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