НЕЧІТКИЙ НЕЛІЙНИЙ БЕГГІНГ НА ОСНОВІ АДАПТИВНОЇ МЕТАМОДЕЛІ В ЗАДАЧАХ ПРОГНОЗУВАНЬ

Prediction is a fundamental task in artificial intelligence, applied across various domains from finance to marketing and industry. Traditional linear models often fall short in capturing the complexity of data relationships, necessitating the enhancement of predictive model accuracy and reliability. Nonlinear bagging, based on an adaptive meta-model, has emerged as an effective approach for processing large datasets. This method involves creating an ensemble of models with diverse parameters, ensuring both prediction quality and stability in the face of anomalies. However, avoiding overfitting is crucial, requiring the selection of appropriate optimization strategies, notably utilizing an adaptive meta-model. Further development of this method entails exploring various adaptation and optimization strategies for hyperparameters. Overall, the proposed fuzzy nonlinear online bagging procedure synthesizes the ensemble's computational intelligence within the framework of online data processing, offering advantages in handling both sequential and non-stationary data.