SOURCES AND DATA FOR THE STUDY – VALIDITY OF SCIENTIFIC KNOWLEDGE

Arkhireev Aleksey Vladimirovich

The authors of this article explore the limitations inherent in traditional forecasting methods and advocate the use of more sophisticated, integrative approaches, particularly vector forecasting, to improve the accuracy and reliability of predicting future events. Based on a critical analysis of various fields, including economics, technology and social sciences, where traditional methods have been shown to fail, the study illustrates the risks associated with relying solely on historical data; these methods often fail to account for non-linear, stochastic elements that often affect results. The study emphasises the need for new methodologies that take into account multiple data streams and complex interdependencies between variables. The vector forecasting method, which utilises multivariate data analysis, is seen as a superior alternative that can capture the multifaceted nature of real-world systems. By incorporating different variables and potential scenarios into a holistic predictive model, this method offers significant improvements over linear forecasting methods, which are prone to oversimplifications that can lead to significant forecasting inaccuracies. Recommendations for future research are aimed at further developing and improving these advanced models, exploring their interdisciplinary applicability, and improving their robustness by utilising advanced technologies such as machine learning and big data analytics. The article argues that moving towards such adaptive and integrative approaches is essential for the scientific community to better navigate the uncertainty of future events and thereby contribute to better informed decision-making in various industries.

Keywords:Forecasting methodologies; vector forecasting; limited historical data; multivariate data analysis; nonlinear dynamics; stochastic elements; forecast accuracy; integrative approaches; machine learning; big data analytics