Intelligent Systems with Applications, Год журнала: 2023, Номер 18, С. 200202 - 200202

Опубликована: Фев. 15, 2023

In this paper, stock price data has been predicted using several state-of-the-art methodologies such as stochastic models, machine learning techniqus, and deep algorithms. An efficient decomposition method resonating with these Machine Intelligence (MI) models embedded boosting ensemble method. Finally a Model Confidence Set (MCS) based algorithm proposed for forecasting data. Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) decomposed orthogonal subseries have Random Forests (RFs). Then Kernel Ridge Regression (KRR) model is used to combine those predictions form hybrid predictor. addition, improvement in prediction performance observed kernel functions. Boosting (AdaBoost) found stimulating accuracy of Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) models. CEEMDAN also increased the AdaBoost. Nevertheless, combination forecasts from various good approach improving result. Despite optimizing weights all heuristic MCS-based snuffing least important prior averaging conceded potent approach. MCS rescinds insignificant on out-of-sample or in-sample equally average superior The compared existing standalone techniques validation measures. However, Support Vector (CCEMDAN_SVR) be best predictor current scenario.

Язык: Английский