METHOD FOR DETERMINING THE STRUCTURE OF NONLINEAR MODELS FOR TIME SERIES PROCESSING
DOI:
https://doi.org/10.15588/1607-3274-2025-1-5Keywords:
data science, statistical learning, time series,, nonlinear models, numerical methods, least square methodAbstract
Context. The practice of today’s problems actualizes the increase in requirements for the accuracy, reliability and completeness of the results of time series processing in many applied areas. One of the methods that provides high-precision processing of time series with the introduction of a stochastic model of measured parameters is statistical learning methods. However, modern approaches to statistical learning are limited, for the most part, to simplified polynomial models. Practice proves that real data most often have a complex form of a trend component, which cannot be reproduced by polynomials of even a high degree. Smoothing of nonlinear models can be implemented by various approaches, for example, by the method of determining the parameters of nonlinear models using the differential spectra balance (DSB) in the scheme of differential-non-Taylor transformations (DNT). The studies proved the need for its modification in the direction of developing a conditional approach to determining the structure of nonlinear mathematical models for processing time series with complex trend dynamics.
Objective. The development of a method for determining the structure of nonlinear by mathematical models for processing time series using DSB in DNT transformations.
Method. The paper develops a method for constructing nonlinear mathematical models in the DNT transformation scheme. The modification of the method consists in controlling the conditions for the formation of a certain system of equations in the DSB scheme to search for the parameters of a nonlinear model with its analytical solutions. If the system is indeterminate, the nonlinear model is supplemented by linear components. In the case of an overdetermined system, its solution is carried out using the least squares norm. A defined system is solved by classical approaches. These processes are implemented with the control of stochastic and dynamic accuracy of models in the areas of observation and extrapolation. If the results of statistical learning are unsatisfactory in accuracy, the obtained values of the nonlinear model are used as initial approximations of numerical methods.
Result. Based on carried-out research, a method for determining the structure of nonlinear models for processing time series using BDS in the scheme of DNT transformations is proposed. Its application provides a conditional approach to determining the structure of models for processing time series and increasing the accuracy of estimation at the interval of observation and extrapolation.
Conclusions. The application of the proposed method for determining the structure of nonlinear models for processing time series allows obtaining models with the best predictive properties in terms of accuracy
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