informИнформатикаInformatics1816-03012617-6963UIIP NASB10.37661/1/1816-0301-2026-23-3-92-106inform-1404Research ArticleИНТЕЛЛЕКТУАЛЬНЫЕ СИСТЕМЫINTELLIGENT SYSTEMSВероятностный трансформер для предиктивного мониторинга редакционного процессаA probabilistic transformer for predictive monitoring of editorial processhttps://orcid.org/0009-0004-5347-8806СьяновД. А.SyanovD. A.

Сьянов Дмитрий Андреевич , старший преподаватель кафедры веб-технологий и компьютерного моделирования

пр. Независимости, 4, Минск, 220030

Dmitry A. Syanov , Senior Lecturer, Department of Web Technologies and Computer Modeling

av. Nezavisimosti 4, Minsk, 220030

syanov@bsu.byБелорусский государственный университетБеларусьBelarusian State UniversityBelarus20262609202623392106Copyright © Сьянов Д.А., 20262026Сьянов Д.А.Syanov D.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://inf.grid.by/jour/article/view/1404Цели

Цели. Целью исследования является разработка подхода к прогнозированию сроков наступления нескольких ключевых точек в многоэтапных процессах.

Методы

Методы. Предложен метод, основанный на обработке последовательностей событий с учетом стадий, промежутков времени и сведений об участниках процесса, а также на согласованном учете взаимосвязей между прогнозируемыми этапами.

Результаты

Результаты. Проведенные эксперименты на данных редакционного документооборота показали повышение точности по сравнению с рекуррентными нейронными сетями и существенное преимущество на ранних этапах по сравнению с моделями градиентного бустинга.

Заключение

Заключение. Метод позволяет получать согласованные интервальные прогнозы для нескольких этапов процесса и может использоваться для оперативного обновления оценок без применения специализированного оборудования.

Objectives

Objectives. The aim of this study is to develop an approach for predicting the timing of several key stages in multi-stage processes.

Methods

Methods. A method is proposed based on processing event sequences taking into account stages, time intervals, and information about process participants, as well as a consistent consideration of the relationships between the predicted stages.

Results

Results. Experiments conducted on editorial document flow data demonstrated improved accuracy compared to recurrent neural networks and a significant advantage in the early stages compared to gradient boosting models.

Conclusion

Conclusion. The method allows obtaining of consistent interval forecasts for several stages of a process and can be used to quickly update the estimates without the use of specialized equipment.

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The authors declare that there are no conflicts of interest present.