Explainability Requirements for Time Series Forecasts: A Study in the Energy Domain

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OriginalspracheEnglisch
Titel des SammelwerksProceedings
Untertitel32nd IEEE International Requirements Engineering Conference, RE 2024
Herausgeber/-innenGrischa Liebel, Irit Hadar, Paola Spoletini
Herausgeber (Verlag)IEEE Computer Society
Seiten229-239
Seitenumfang11
ISBN (elektronisch)9798350395112
ISBN (Print)979-8-3503-9512-9
PublikationsstatusVeröffentlicht - 2024
Veranstaltung32nd IEEE International Requirements Engineering Conference, RE 2024 - Reykjavik, Island
Dauer: 24 Juni 202428 Juni 2024
Konferenznummer: 32

Publikationsreihe

NameProceedings of the IEEE International Conference on Requirements Engineering
ISSN (Print)1090-705X
ISSN (elektronisch)2332-6441

Abstract

With the rise of artificial intelligence in industry, many companies rely on machine learning methods such as time series forecasting. By processing data from the past, such systems can provide predictions for data in the future. In practice, however, there is often skepticism about the quality of the forecasts. Explainability has been identified as a means to address this skepticism and foster trust. While there are already different methods to explain time series forecasts, it is unclear which of these explanations are actually useful for stakeholders. To investigate the need for explanations for time series forecasts, we conducted a study at a mid-sized German company in the energy domain. Throughout the study, 23 participants were shown five examples of different explanation types. For each type of explanation, we tested if it actually helped our participants to better understand the forecasts. We found that visual explanations including decision trees and feature importance charts were able to improve domain experts' understanding of time series forecasts. Textual explanations tended to lead to confusion rather than empowerment. While the exact findings and preferable types of explanations may vary between companies, our concrete results can provide a starting point for in-depth analyses in other environments.

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Explainability Requirements for Time Series Forecasts: A Study in the Energy Domain. / Droste, Jakob; Fuchs, Ronja; Deters, Hannah et al.
Proceedings : 32nd IEEE International Requirements Engineering Conference, RE 2024. Hrsg. / Grischa Liebel; Irit Hadar; Paola Spoletini. IEEE Computer Society, 2024. S. 229-239 (Proceedings of the IEEE International Conference on Requirements Engineering).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Droste, J, Fuchs, R, Deters, H, Klunder, J & Schneider, K 2024, Explainability Requirements for Time Series Forecasts: A Study in the Energy Domain. in G Liebel, I Hadar & P Spoletini (Hrsg.), Proceedings : 32nd IEEE International Requirements Engineering Conference, RE 2024. Proceedings of the IEEE International Conference on Requirements Engineering, IEEE Computer Society, S. 229-239, 32nd IEEE International Requirements Engineering Conference, RE 2024, Reykjavik, Island, 24 Juni 2024. https://doi.org/10.1109/RE59067.2024.00030
Droste, J., Fuchs, R., Deters, H., Klunder, J., & Schneider, K. (2024). Explainability Requirements for Time Series Forecasts: A Study in the Energy Domain. In G. Liebel, I. Hadar, & P. Spoletini (Hrsg.), Proceedings : 32nd IEEE International Requirements Engineering Conference, RE 2024 (S. 229-239). (Proceedings of the IEEE International Conference on Requirements Engineering). IEEE Computer Society. https://doi.org/10.1109/RE59067.2024.00030
Droste J, Fuchs R, Deters H, Klunder J, Schneider K. Explainability Requirements for Time Series Forecasts: A Study in the Energy Domain. in Liebel G, Hadar I, Spoletini P, Hrsg., Proceedings : 32nd IEEE International Requirements Engineering Conference, RE 2024. IEEE Computer Society. 2024. S. 229-239. (Proceedings of the IEEE International Conference on Requirements Engineering). doi: 10.1109/RE59067.2024.00030
Droste, Jakob ; Fuchs, Ronja ; Deters, Hannah et al. / Explainability Requirements for Time Series Forecasts : A Study in the Energy Domain. Proceedings : 32nd IEEE International Requirements Engineering Conference, RE 2024. Hrsg. / Grischa Liebel ; Irit Hadar ; Paola Spoletini. IEEE Computer Society, 2024. S. 229-239 (Proceedings of the IEEE International Conference on Requirements Engineering).
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