Search

Search Results

• FORECASTING STUDENT MOVEMENT USING MACHINE LEARNING AND TIME SERIES ANALYSIS

  Mirziyod Adkham ugli Radjapov , К.D. Chemukhin , L. E. Petrosyan

  134-151

  2026-07-07

  Abstract ▼

  Managing student mobility amid demographic fluctuations and the digitalization of higher education is becoming a key factor in university sustainability, affecting both financial performance and the quality of the educational process. The increasing complexity of processes such as admissions, withdrawals, academic leaves of absence, transfers, and reinstatements requires a shift from expert assessments to formalized models and predictive analytics based on the processing of large datasets. The aim of this study is to develop and evaluate the effectiveness of a model for forecasting student population dynamics based on machine learning algorithms and using time series analysis. Aggregated statistical data on student mobility at Russian and Chinese universities for the period 2013–2024 were used as the empirical basis, which allowed for consideration of both the structural features of national higher education systems and long-term trends and anomalous events (including the impact of the COVID-19 pandemic). The methodological framework includes a dynamic student cohort balance model in the form of a system of recurrent equations describing transitions between academic years and enrollment statuses, and an additive Prophet model used for independent forecasting of key flows (admissions, transfers, withdrawals, academic leaves of absence, reinstatements) as separate time series. The software implementation is based on the FastAPI–React stack, utilizing the SQLAlchemy ORM layer and mechanisms for caching the results of predictive calculations, which ensures high performance when processing queries. Experimental results on real data demonstrate the robustness of the developed model to nonlinear changes in the input series and confirm the feasibility of integrating machine learning into the student movement management system. The practical significance of this work lies in the creation of an information and analytical system that provides automated monitoring and forecasting of student movement trajectories between courses and statuses, enabling universities to transition from reactive to proactive planning of admissions campaigns, classroom allocation, and the distribution of personnel and infrastructure resources.

• A TIME SERIES FORECASTING METHOD BASED ON COGNITIVE FUZZY MODELING AND REGRESSION ANALYSIS

  А.I. Guseva , R.М. Romanov

  157-178

  2025-12-30

  Abstract ▼

  The relevance of the study stems from the low effectiveness of traditional time series forecasting methods under conditions of high uncertainty and limited data, which are typical of weakly formalized systems. The aim of the work is to develop and substantiate a time series forecasting method based on a hybrid approach that integrates cognitive fuzzy modelling, regression analysis, and the analytic network process. Within the study, a systematic review and comparative analysis of existing forecasting methods was carried out, including approaches based on fuzzy logic, neural network and cognitive modelling, as well as ensemble and hybrid methods, and their limitations were identified when dealing with small samples, nonlinear dependencies, and uncertainty. The proposed method includes: the construction of fuzzy cognitive maps, defuzzification of linguistic assessments, clustering of factors, application of the analytic network process to determine priorities, and the formation of a weighted regression model. The model undergoes statistical validation using the , , , and  metrics, as well as diagnostic checks of the assumptions underlying regression analysis, including tests for multicollinearity and autocorrelation. Application of the method reduced  from 0.38 to 0.22,  from 0.30 to 0.18, and  from 11.65 % to 7.12 %, thereby confirming an improvement in the accuracy and robustness of forecasts under limited data compared with classical multiple regression. The novelty of the proposed method lies in the integration of cognitive modelling, regression analysis, and the analytic network process, whereby the strengths of each component compensate for their individual limitations, providing more accurate and robust forecasting under the uncertainty inherent in the system under study. The practical significance of the work consists in the possibility of applying the proposed method to support decision-making and to enhance the validity of forecasts in various subject domains and situations characterized by a limited number of observations, a substantial role of expert judgments, and a complex structure of causal relationships between indicators over time