Abstract
The aim of this paper is to forecast the monthly unemployment rate’s time series using deep learning algorithms. Based on data from five Central European countries, we tested the forecasting performance of the ‘conventional’ Box–Jenkins methodology in comparison with three deep learning models: the CNN (Convolutional Neural Network), the MLP (Multilayer Perceptron) and the random forest algorithm. The MAPE, MAE, RRMSE, and MSE error tests were used for testing the forecasting results. In our results, the ARIMA model was outperformed by one of the deep learning algorithms in all cases. The medium-term predictions suggest that in the Central European area, unemployment will remain relatively high in the future.
Publisher
Riga Technical University
Reference50 articles.
1. Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685–725. https://doi.org/10.1146/annurev-economics-080217-053433
2. Borghi, P. H., Zakordonets, O., & Teixeira, J. P. (2021). A COVID-19 time series forecasting model based on MLP ANN. Procedia Computer Science, 181, 940–947. https://doi.org/10.1016/j.procs.2021.01.250
3. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/a:1010933404324
4. Brownlee, J. (2018). Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery.
5. Celbiş, M. G. (2023). Unemployment in Rural Europe: A Machine Learning Perspective. Applied Spatial Analysis and Policy, 16, 1071–1095. https://doi.org/10.1007/s12061-022-09464-0