Forecasting Unemployment Rate for Multiple Countries Using a New Method for Data Structuring

Abstract

Forecasting the Unemployment Rate (UR) plays a key role in shaping economic policies and development strategies. While most research focuses on predicting UR for individual countries, there has been limited progress in creating a unified forecasting model that works across multiple countries. Traditional time series methods are usually designed for single-country data, making it difficult to develop a model that handles data from various regions. This study presents a new data structuring technique that divides time series into smaller segments, enabling the development of a single model applicable to 44 countries using various economic indicators. Four forecasting models were tested: an artificial neural network (ANN), a hybrid ANN with machine learning (ML), a genetic algorithm-optimized ANN (ANN-GA), and a linear regression model. The linear regression model, which used lagged UR values, delivered the best results with an R² of 0.964 and 89.8% accuracy. The ANN-GA model also performed strongly, achieving an R² of 0.945 and 85.1% accuracy. These results highlight the effectiveness of the proposed data structuring method, demonstrating that a single model can accurately forecast multiple time series across different regions.