Measures for combining prediction intervals uncertainty and reliability in forecasting

Gomes de Almeida, Vania ORCID: https://orcid.org/0000-0002-2185-7850 and Gama, João (2016) Measures for combining prediction intervals uncertainty and reliability in forecasting. Burduk, Robert, Jackowski, Konrad, Kurzyński, Marek, Woźniak, Michał and Żołnierek, Andrzej, eds. Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. In: 9th International Conference on Computer Recognition Systems CORES 2015, 25-27 May 2015, Wroclaw, Poland. ISBN 9783319262253, e-ISBN 9783319262277. ISSN 2194-5357 [Conference or Workshop Item] (doi:10.1007/978-3-319-26227-7_14)

Abstract

In this paper we propose a new methodology for evaluating prediction intervals (PIs). TypicallyAlmeida, V. , PIs areGama, J. evaluated with reference to confidence values. However, other metrics should be considered, since high values are associated to too wide intervals that convey little information and are of no use for decision-making. We propose to compare the error distribution (predictions out of the interval) and the maximum mean absolute error (MAE) allowed by the confidence limits. Along this paper PIs based on neural networks for short-term load forecast are compared using two different strategies: (1) dual perturb and combine (DPC) algorithm and (2) conformal prediction. We demonstrated that depending on the real scenario (e.g., time of day) different algorithms perform better. The main contribution is the identification of high uncertainty levels in forecast that can guide the decision-makers to avoid the selection of risky actions under uncertain conditions. Small errors mean that decisions can be made more confidently with less chance of confronting a future unexpected condition.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Natural Sciences
Item ID: 23733
Useful Links:
Depositing User: Vania Gomes De almeida
Date Deposited: 05 Mar 2018 17:24
Last Modified: 20 Aug 2020 12:09
URI: https://eprints.mdx.ac.uk/id/eprint/23733

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