Predicting the unemployment rate and energy poverty levels in selected European Union countries using an ARIMA-ARNN model

PeerJ Comput Sci. 2023 Jul 10:9:e1464. doi: 10.7717/peerj-cs.1464. eCollection 2023.

Abstract

This article analyzes the correlation between energy poverty percentage and unemployment rate for four European countries, Bulgaria, Hungary, Romania and Slovakia, comparing the results with the European average. The time series extracted from the datasets were imported in a hybrid model, namely ARIMA-ARNN, generating predictions for the two variables in order to analyze their interconnectivity. The results obtained from the hybrid model suggest that unemployment rate and energy poverty percentage have comparable tendencies, being strongly correlated. The forecasts suggest that this correlation will be maintained in the future unless appropriate governmental policies are implemented in order to lower the impact of other aspects on energy poverty.

Keywords: ARIMA-ARNN; Developing/developed countries; Forecasting; Hybrid model; Time series.

Grants and funding

The authors received no funding for this work.