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Hybrid Knowledge Representation for Temporal Forecasting

2026-06Comunicação em conferência Acesso aberto
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dc.contributor.authorCatarro, Pedro Inácio
dc.contributor.authorde Campos Souza, Paulo Vitor
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.date.accessioned2026-07-01T11:03:01Z
dc.date.available2026-07-01T11:03:01Z
dc.date.issued2026-06
dc.descriptionCatarro, P. I., & de Campos Souza, P. V. (2026). Hybrid Knowledge Representation for Temporal Forecasting: Interpretable Symbolic Extraction from ARIMAX and Rule-Based Learning. Paper presented at International Joint Conference on Neural Networks (IJCNN) 2026, Maastricht, Netherlands. https://linklings.s3.amazonaws.com/organizations/WCCI/wcci2026/submissions/stype114/iuy8g-ijcnn_pap4667s2.pdf
dc.description.abstractAccurate electricity demand forecasting increasingly relies on complex machine learning models that lack explicit interpretability. This paper presents a hybrid framework that integrates statistical time-series forecasting with symbolic knowledge extraction to produce human-interpretable representations of climatic demand drivers. Using open-access meteorological and consumption data from multiple Portuguese cities, we train SARIMAX and ensemble models with exogenous inputs and extract logical IF–THEN rules that describe local demand regimes. These rules are then formalized as knowledge graphs encoding variable-level and rule-level causal structures. A case study on Coimbra shows that temperature and day length define compact, physically meaningful consumption regimes, demonstrating that high forecasting accuracy can coexist with explicit symbolic knowledge. The proposed approach provides a reproducible pathway from numerical prediction to formal knowledge representation for energy systems.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent8
dc.format.extent2207477
dc.identifier.otherPURE: 166577199
dc.identifier.otherPURE UUID: cc2ec147-e56d-4aa0-af98-4a20887796c9
dc.identifier.otherORCID: /0000-0002-7343-5844/work/219405480
dc.identifier.urihttp://hdl.handle.net/10362/204246
dc.identifier.urlhttps://github.com/SlyCooper123
dc.identifier.urlhttps://linklings.s3.amazonaws.com/organizations/WCCI/wcci2026/submissions/stype114/iuy8g-ijcnn_pap4667s2.pdf
dc.language.isoeng
dc.peerreviewedyes
dc.relationhttps://doi.org/10.54499/UID/04152/2025
dc.relationhttps://doi.org/10.54499/UID/PRR/04152/2025
dc.titleHybrid Knowledge Representation for Temporal Forecastingen
dc.title.subtitleInterpretable Symbolic Extraction from ARIMAX and Rule-Based Learningen
dc.typeconference paper
degois.publication.titleInternational Joint Conference on Neural Networks (IJCNN) 2026
dspace.entity.typePublication
rcaap.rightsopenAccess

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