The article is devoted to the development of combined models, methods and tools designed to solve the current problems of modeling and analysis of monitoring process data, which are repre-sented by time series and differ in variable or fuzzy observation intervals (CHRPNI). In the article, a new relational separable model (RSM) and a combined quantile algorithm are proposed to in-crease the accuracy and efficiency of modeling and analysis of the processes of CHRPNI. The rela-tional model is defined by a system of fuzzy relational relations of the first and second order ob-tained on the basis of the original sequence of data. In the combined algorithm, the results of calcu-lations obtained by SPM and models of fuzzy relational relationships were generalized with the op-timal selection of weighting factors for individual components. As a result of the conducted research by means of numerical modeling, it was established that the introduction of combined process models in the case of PNEU is rational and effective. Exam-ples of data analysis of monitoring processes of rehabilitation of diabetic patients showed certain possibilities of ensuring the accuracy of the results of the analysis of indicators and their short-term forecasting.