Adaptive Regression Model Construction Based on the Functional Quality Analysis of the Sequence Segment Processing

The article considers the problem of constructing an adaptive model aimed at improving the quality indicators of processing information sequences. In data processing techniques that have found application in many application areas, the applied analysis of observation objects is computationally resource-intensive and requires many iterations in case of changes in data properties. The article proposes a technique for selecting segments of an information sequence obtained in different ways, which differs in the use of the quality functional of regression models for processing subsequences. The sequences of observation objects received at the input of the model are divided by various specified segmentation algorithms. Pre-selected regression models are trained on each obtained segment and, depending on the obtained values of the calculated quality functional, the best models in terms of quality indicators are assigned to the segments. This allows us to form an aggregation model for data processing. Based on the experiment on model data and samples, the proposed technique is assessed. The values of the quality indicator MSE and MAE are obtained for different processing algorithms and with a different number of segments. The proposed method makes it possible to increase the MSE and MAE indicators by segmentation and assignment of regression models that have the best indicators on individual segments. The proposed solution is aimed at further improvement of ensemble methods. Its application allows to increase the efficiency of setting up basic algorithms in case of data property transformation and to improve the interpretability of results. The method can be used in developing models and methods for processing information sequences.