Identifying weak signals in the labor market: a machine learning approach for strategic policymaking

This study introduces a novel machine learning-based methodology for detecting and forecasting the strength of weak signals in the labor market, using Greece as a case study and utilizing Eurostat time series data (2000–2023). Weak signals, conceptualized as subtle anomalies within otherwise stable labor market indicators, were identified through the Isolation Forest algorithm and projected using a Long Short-Term Memory neural network model. Findings highlight structural instability in male manufacturing employment and wholesale/retail trade, contrasted by stable trends in sectors such as agriculture, education, and public administration. This study contributes to labor market foresight by integrating anomaly detection with predictive analytics, offering valuable insights for proactive, scenario-based policy design in support of a sustainable and adaptive future of work.