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Explainable weather forecasts through an LSTM-CBR twin system.

Pirie, Craig; Suresh, Malavika; Salimi, Pedram; Palihawadana, Chamath; Nanayakkara, Gayani

Authors



Contributors

Pascal Reuss
Editor

Jakob Schönborn
Editor

Abstract

In this paper, we explore two methods for explaining LSTM-based temperature forecasts using previous 14 day progressions of humidity and pressure. First, we propose and evaluate an LSTM-CBR twin system that generates nearest-neighbors that can be visualised as explanations. Second, we use feature attributions from Integrated Gradients to generate textual explanations that summarise the key progressions in the past 14 days that led to the predicted value.

Citation

PIRIE, C., SURESH, M., SALIMI, P., PALIHAWADANA, C. and NANAYAKKARA, G. 2022. Explainable weather forecasts through an LSTM-CBR twin system. In Reuss, P. and Schönborn, J. (eds.) Workshop proceedings of the 30th International conference on case-based reasoning (ICCBR-WS 2022), 12-15 September 2022, Nancy, France. CEUR workshop proceedings, 3389. Aachen: CEUR-WS [online], pages 256-260. Available from: https://ceur-ws.org/Vol-3389/ICCBR_2022_XCBR_Challenge_RGU.pdf

Conference Name Workshops of the 30th International conference on case-based reasoning (ICCBR-WS 2022)
Conference Location Nancy, France
Start Date Sep 12, 2022
End Date Sep 15, 2022
Acceptance Date Jul 22, 2022
Online Publication Date May 11, 2023
Publication Date May 11, 2023
Deposit Date Jun 2, 2023
Publicly Available Date Mar 29, 2024
Publisher CEUR Workshop Proceedings
Pages 256-260
Series Title CEUR workshop proceedings
Series Number 3389
Series ISSN 1613-0073
Keywords LSTM; XCBR; NLG; Integrated gradients; Forecasting; Visualisation
Public URL https://rgu-repository.worktribe.com/output/1977757
Publisher URL https://ceur-ws.org/Vol-3389/ICCBR_2022_XCBR_Challenge_RGU.pdf

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