On Accuracy of Long-Term Risk Forecasts by Normal Variance-Mean Mixtures Decomposition Algorithm* View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-10

AUTHORS

A.Yu. Korchagin

ABSTRACT

This article provides an accuracy and applicability analysis of the approach to risk forecasting using parametric mixture models. The studied method is based upon results of the modified grid-based two-step decomposition algorithm for variance-mean mixtures. Instead of setting a fixed forecast interval, an approach is introduced to dynamically monitor relevant metrics for forecasts in a wide time frame, producing the basis for decision making regarding the quality and reliability of predictions for certain periods of time. More... »

PAGES

287-297

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10958-016-3030-8

DOI

http://dx.doi.org/10.1007/s10958-016-3030-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1030175308


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