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Prediction of Unemployment Rates with Time Series and Machine Learning Techniques

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Abstract

In this paper, are explored and analyzed time series and machine learning models for prediction of unemployment in several countries (Med, Baltic, Balkan, Nordic, Benelux) for different forecasting horizons. FARIMA is a suitable model when long memory exists in a time series and has been applied successfully for predicting unemployment. To overcome the potential issue of heteroskedasticity, we explore whether FARIMA models with GARCH errors achieve more accurate results. To further improve forecasting accuracy, we consider models with non-normal errors. The above models however cannot take into account the non-linearity of the data and due to this fact, we employ three machine learning techniques to forecast unemployment rates, i.e. fully connected feed forward neural networks, support vector regression and multivariate adaptive regression splines. ARIMA and Holt-Winters are considered as benchmark models. Finally, the effects of different forecasting horizons and different geographic locations in terms of forecasting accuracy of the models are explored.

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Correspondence to Christos Katris.

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Appendices

Appendix

1-Step Ahead Predictions

See Tables 12 and 13.

Table 12 Positions of models RMSE (MAE)
Table 13 Prediction accuracy

3-Step Ahead Predictions

See Tables 14 and 15.

Table 14 Prediction accuracy
Table 15 Positions of models RMSE (MAE)

12-Step Ahead Predictions

See Tables 16 and 17.

Table 16 Prediction accuracy
Table 17 Positions of models RMSE (MAE)

Data for Friedman Tests

Elements of the Model

Dependent variable # of times where the model is included in the MCS superior set of models.

Factor 1 Horizon (1, 3 and 12 steps ahead).

Factor 2 Geographic area (Balkan, Med, Nordic, Baltic, Benelux).

See Tables 18 and 19.

Table 18 Prediction accuracy
Table 19 # of times where the model is included in the MCS superior set of models

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Katris, C. Prediction of Unemployment Rates with Time Series and Machine Learning Techniques. Comput Econ (2019) doi:10.1007/s10614-019-09908-9

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Keywords

  • FARIMA/GARCH
  • FARIMA
  • Neural networks
  • Support vector machines
  • Multivariate adaptive regression splines
  • Multiple steps ahead predictions
  • Forecasting accuracy