Distributional time series for forecasting and risk assessment
by Boris S. Dobronets; Olga A. Popova; Alexei M. Merko
International Journal of Risk Assessment and Management (IJRAM), Vol. 24, No. 2/3/4, 2021

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.

Online publication date: Wed, 26-Oct-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Risk Assessment and Management (IJRAM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com