Predicting Extreme Events for Complex High-Dimensional Systems

Journal of Finance and Data Science. 2025.

Чертоганов К. А.

This research aims to enhance the forecasting accuracy of extreme events, which pose significant challenges across various domains such as meteorology, finance, and public health. The study investigates the integration of cross-correlation and partial autocorrelation functions (PACF) with machine learning techniques to address the limitations of traditional forecasting methods and improve predictive reliability and interpretability.

Ключевые слова: forecasting, time series, cross-correlation, Machine Learning, Extreme Event Prediction, Uncertainty Quantification, High-dimensional systems, partial autocorrelation