The minimizing sets method for trend detection in time series of noisy measurement data

This article discusses the problem of trend detection in time series generated by technical devices. The solution to this problem is closely related to the problem of detecting coarse measurements (outliers), which negatively impact the accuracy of estimates of various physical quantities. These are crucial in many applications in various scientific fields in which the input data are observations, such as space geodynamics, geodesy, and others. Previously, the author proposed a trend-detecting method based on the condition of maximizing the amount of data cleared of outliers and used in further processing. The reference values used for trend construction are determined as a result of a completely convergent iterative process, the core of which is the minimizing sets method developed earlier by the author. This paper deals with the aspects of trend construction in the class of harmonic functions with unknown frequencies, phases and amplitudes.The main problem of trend construction in such a functional class is the nonlinear dependence of harmonics on the desired parameters, which does not allow to reduce the problem of trend search to the solution of a system of linear equations. The search for harmonics approximating the measurement data is carried out by the conjugate gradients method generalized to nonlinear problems. The efficiency of the method was tested on the test problem of trend construction in the data obtained by computer simulation.