Сибирский федеральный университет

Тип публикации: статья из журнала

Год издания: 2022

Идентификатор DOI: 10.1504/ijram.2021.126412

Ключевые слова: aggregation, computational probabilistic analysis, distributional time series, forecasting, risk assessment

Аннотация: Important computational aspects of big data processing and forecasting methods for the problems of the risk assessment are under consideration. A new approach to the study and forecasting of big data represented by time series is discussed. Our approach is based on Big Data technologies, including data aggregation procedures for input and output parameters and computational probabilistic analysis. The result of this approach is a new type of representation of a big time series in the form of distributional time series. Piecewise polynomial models are used for data aggregation procedures. To solve computational problems on distributed time series, we developed arithmetic over piecewise polynomial functions. To demonstrate our approach, we studied the problem of risk assessment for investment projects. Copyright © 2021 Inderscience Enterprises Ltd.

Журнал: International Journal of Risk Assessment and Management

Выпуск журнала: Vol. 24, Is. 2-4

Номера страниц: 140-155

ISSN журнала: 14668297

Издатель: Inderscience Publishers

• Dobronets B.S. (Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, 660074, Russian Federation)
• Popova O.A. (Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, 660074, Russian Federation)
• Merko A.M. (Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, 660074, Russian Federation)

• Scopus