ENHANCING ECONOMIC AND MATHEMATICAL MODELING FOR STRATEGIC FORECASTING IN CONSTRUCTION ENTERPRISE MANAGEMENT

This article is devoted to the issues of improving economic-mathematical models for forecasting the performance of construction companies. Factors such as price volatility in the construction sector, project timelines, and resource allocation complicate the pre-estimation of project costs and durations. Based on an analysis of existing models, the article proposes the enhancement of hybrid approaches based on machine learning and econometric methods. The research results indicate that the proposed models can increase forecasting accuracy by 15-20%, enabling construction companies to optimize costs and mitigate risks.

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