Prediction interval modeling using Gaussian process quantile regression
Author(s)
Aguilar Fargas, Joan
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Other Contributors
System Design and Management Program.
Advisor
Moshe E. Ben-Akiva and Francisco C. Pereira.
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In this thesis a methodology to construct prediction intervals for a generic black-box point forecast model is presented. The prediction intervals are learned from the forecasts of the black-box model and the actual realizations of the forecasted variable by using quantile regression on the observed prediction error distribution, the distribution of which is not assumed. An independent meta-model that runs in parallel to the original point forecast model is responsible for learning and generating the prediction intervals, thus requiring no modification to the original setup. This meta-model uses both the inputs and output of the black-box model and calculates a lower and an upper bound for each of its forecasts with the goal that a predefined percentage of future realizations are included in the interval formed by both bounds. Metrics for the performance of the meta-model are established, paying special attention to the conditional interval coverage with respect to both time and the inputs. A series of cases studies are performed to determine the capabilities of this approach and to compare it to standard practices.
Description
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014. Cataloged from PDF version of thesis. Includes bibliographical references (pages 62-65).
Date issued
2014Department
System Design and Management Program.; Massachusetts Institute of Technology. Engineering Systems DivisionPublisher
Massachusetts Institute of Technology
Keywords
Engineering Systems Division., System Design and Management Program.