HYBRID MACHINE LEARNING METHODS FOR DECISION SUPPORT IN AUTOMATED INFORMATION SYSTEMS

This article is dedicated to the development and research of an advanced hybrid machine learning method for time series forecasting in decision support systems (DSS). The relevance of the work is driven by the rapid growth of data volumes in modern information systems, particularly in cloud infrastructures, and the need for accurate forecasting tools for effective resource management. The objective of the study is to increase the accuracy of computing resource load forecasting by developing a hybrid model that combines the advantages of statistical methods and deep learning architectures. A novel hybrid architecture is proposed, integrating the Autoregressive Integrated Moving Average (ARIMA) model for modeling linear components of a time series, and a Long Short-Term Memory (LSTM) recurrent neural network with a built-in Attention Mechanism for analyzing non-linear residuals. The ARIMA model is used to capture stationary dependencies and seasonality, while the LSTM network with an attention mechanism effectively models complex, non-linear, and long-term patterns in the data remaining after ARIMA processing. An experimental study was conducted on a real dataset of CPU utilization monitoring from virtual machines in the AWS (Amazon Web Services) cloud environment. The proposed hybrid ARIMA-LSTM model with an attention mechanism demonstrated a significant improvement in forecasting accuracy compared to baseline models: pure ARIMA, pure LSTM, and a standard hybrid ARIMA-LSTM model. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics for the developed model were 12-18% lower than those of the best-performing baseline model. Scientific novelty lies in the enhancement of existing hybrid approaches by integrating an attention mechanism into the LSTM architecture for analyzing time series residuals. Practical significance of the work consists in the potential for implementing the developed method in automated DSS for optimizing resource allocation, auto-scaling, preventing overloads, and reducing operational costs of cloud infrastructure.