Sequential analysis of serial measurements based on multivariate reference regions

Sequential data series analysis procedures are considered. An approach is developed when a set of multivariate features of a certain object, which varies in time, is presented as a single vector of observed values. By increasing the dimensionality of the data, it is possible to obtain a single picture of the description of objects, to take into account the objectively existing dependence of individual observations, and to simulate changes over time. The basis for solving classification problems is the use of multivariate reference regions. Three options for data processing procedures are proposed, their properties are investigated, and recommendations for practical application are developed. To demonstrate the capabilities of these procedures, the task of early diagnosis of cancer using the PSA (prostate-specific antigen) biomarker is considered. Features of the application of sequential methods for analyzing data series are indicated, recommendations for their effective use are formed, and the advantages of the consolidating approach in data analysis are identified.