• Ye. V. Bodyanskiy Kharkiv National University of Radio Electronics, Kharkiv, Ukraine , Ukraine
  • Kh. V. Lipianina-Honcharenko West Ukrainian National University, Ternopil, Ukraine, Ukraine
  • A. O. Sachenko West Ukrainian National University, Ternopil, Ukraine, Ukraine




ensemble, metamodels, boosting, bagging, multivariate signals, nonstationarity, forecasting


Context. In this research, we explore an ensemble of metamodels that utilizes multivariate signals to generate forecasts. The ensemble includes various traditional forecasting models such as multivariate regression, exponential smoothing, ARIMAX, as well as nonlinear structures based on artificial neural networks, ranging from simple feedforward networks to deep architectures like LSTM and transformers.

Objective. A goal of this research is to develop an effective method for combining forecasts from multiple models forming metamodels to create a unified forecast that surpasses the accuracy of individual models. We are aimed to investigate the effectiveness of the proposed ensemble in the context of forecasting tasks with nonstationary signals.

Method. The proposed ensemble of metamodels employs the method of Lagrange multipliers to estimate the parameters of the metamodel. The Kuhn-Tucker system of equations is solved to obtain unbiased estimates using the least squares method. Additionally, we introduce a recurrent form of the least squares algorithm for adaptive processing of nonstationary signals.

Results. The evaluation of the proposed ensemble method is conducted on a dataset of time series. Metamodels formed by combining various individual models demonstrate improved forecast accuracy compared to individual models. The approach shows effectiveness in capturing nonstationary patterns and enhancing overall forecasting accuracy.

Conclusions. The ensemble of metamodels, which utilizes multivariate signals for forecast generation, offers a promising approach to achieve better forecasting accuracy. By combining diverse models, the ensemble exhibits robustness to nonstationarity and improves the reliability of forecasts.

Author Biographies

Ye. V. Bodyanskiy, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

Dr. Sc., Professor, Professor of the Artificial Intelligence Department

Kh. V. Lipianina-Honcharenko, West Ukrainian National University, Ternopil, Ukraine

PhD, Associate Professor, Associate Professor of the Department for Information Computer Systems and Control

A. O. Sachenko, West Ukrainian National University, Ternopil, Ukraine

Dr. Sc., Professor, Director of the Research Institute for Intelligent Computer Systems


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How to Cite

Bodyanskiy, Y. V., Lipianina-Honcharenko, K. V., & Sachenko, A. O. (2024). ENSEMBLE OF ADAPTIVE PREDICTORS FOR MULTIVARIATE NONSTATIONARY SEQUENCES AND ITS ONLINE LEARNING. Radio Electronics, Computer Science, Control, (4), 91. https://doi.org/10.15588/1607-3274-2023-4-9



Neuroinformatics and intelligent systems