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We study a system that fulfills the class of driving systems developed by A. P. Buslaev (Buslaev networks). In this system, in each of two closed loops there is a segment called a cluster, and it moves at a constant speed if there are no delays. The lengths of the clusters are $l_1^{}$ and $l_2^{}$. There are two common points of the contours, called nodes. Delays in the movement of clusters are due to the fact that two clusters cannot pass through a node at the same time. The contours have the same height, the glazing is accepted. The nodes divide each contour into parts, the length of one of which is equal to $d_i^{}$, and the other $1-d_i^{}$, $i=1,\,2$, — contour number. Studies of the spectrum of average speeds of systems, i.\,e. set of pairs of results $(v_1^{},\,v_2^{})$, where $v_i^{}$ — cluster of average movement speed $i$ taking into account delays, for different initial states and fixed values $l_1^{}$, $l_2^{}$, $d_1^{}$, $d_2^{}$. 12 scenarios of system behavior have been identified, and for each of these manifestations sufficient conditions for its implementation have been found, and each of these observed spectra contains one or two pairs of average velocities.

Processing of large amounts of data often consists of several steps, e.g. pre- and post-processing stages, which are executed sequentially with data written to disk after each step, however, when pre-processing stage for each task is different the more efficient way of processing data is to construct a pipeline which streams data from one stage to another. In a more general case some processing stages can be factored into several parallel subordinate stages thus forming a distributed pipeline where each stage can have multiple inputs and multiple outputs. Such processing pattern emerges in a problem of classification of wave energy spectra based on analytic approximations which can extract different wave systems and their parameters (e.g. wave system type, mean wave direction) from spectrum. Distributed pipeline approach achieves good performance compared to conventional “sequential-stage” processing.

Technological development determines the emergence of highly detailed data in time and space, which expands the possibilities of analysis, allowing us to consider consumer decisions and the competitive behavior of enterprises in all their diversity, taking into account the context of the territory and the characteristics of time periods. Despite the promise of such studies, they are currently limited in the scientific literature. This is due to the range of problems, the solution of which is considered in this paper. The article draws attention to the complexity of the analysis of depersonalized high-frequency data and the possibility of modeling consumption changes in time and space based on them. The features of the new type of data are considered on the example of real depersonalized data received from the fiscal data operator “First OFD” (JSC “Energy Systems and Communications”). It is shown that along with the spectrum of problems inherent in high-frequency data, there are disadvantages associated with the process of generating data on the side of the sellers, which requires a wider use of data mining tools. A series of statistical tests were carried out on the data under consideration, including a Unit-Root Test, test for unobserved individual effects, test for serial correlation and for cross-sectional dependence in panels, etc. The presence of spatial autocorrelation of the data was tested using modified tests of Lagrange multipliers. The tests carried out showed the presence of a consistent correlation and spatial dependence of the data, which determine the expediency of applying the methods of panel and spatial analysis in relation to high-frequency data accumulated by fiscal operators. The constructed models made it possible to substantiate the spatial relationship of sales growth and its dependence on the day of the week. The limitation for increasing the predictive ability of the constructed models and their subsequent complication, due to the inclusion of explanatory factors, was the lack of open access statistics grouped in the required detail in time and space, which determines the relevance of the formation of high-frequency geographically structured data bases.