NEURAL NETWORK MODELING AND FORECASTING OF IMBALANCES IN UKRAINE’S LABOR MARKET UNDER EXTREME CONDITIONS
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Relevance. The full-scale military invasion of the Russian Federation has caused unprecedented distortions in the labour market of Ukraine. These deformations are characterized by deep sectoral and territorial disproportions, which are caused by mass migration, mobilization, destruction of production, and changes in the structure of labor supply and demand. This causes an urgent need to develop tools to quantify and predict said deformations, which is essential for making informed decisions. The purpose of this research is to develop and test a complex technique based on neural network modelling (Long Short-Term. Memory – LSTM). This methodology aims to identify, assess, and forecast labour market deformations and imbalances in Ukraine, and includes the development of a system of criteria for their evaluation. The research methodology is based on an integrated approach that incorporates time series analysis, neural network forecasting (LSTM), methods for detecting structural shifts and anomalies (Isolation Forest), cluster analysis (K-Means), and determination of influencing factors (Random Forest). The research presents a developed system of criteria for assessing war-induced deformations, conducts a quantitative evaluation of sectoral disruptions resulting from the conflict, provides a forecast of imbalance dynamics, and identifies the most vulnerable sectors of the economy. The conclusions emphasise the scientific and practical significance of the developed methodology for monitoring the labour market, as well as for developing adaptive employment policies and programs to support the post-war recovery of the Ukrainian economy. They also demonstrate the potential of neural network models for analysing labour markets under extreme conditions нof uncertainty.
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