Title: Distributional time series for forecasting and risk assessment

Authors: Boris S. Dobronets; Olga A. Popova; Alexei M. Merko

Addresses: Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, 660074, Russia ' Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, 660074, Russia ' Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, 660074, Russia

Abstract: 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.

Keywords: distributional time series; aggregation; computational probabilistic analysis; forecasting; risk assessment.

DOI: 10.1504/IJRAM.2021.126412

International Journal of Risk Assessment and Management, 2021 Vol.24 No.2/3/4, pp.140 - 155

Received: 23 Jul 2020
Accepted: 08 Jan 2021
Published online: 26 Oct 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article