Explainable Time-Series Prediction Using a Residual Network and Gradient-Based Methods
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, H. | - |
dc.contributor.author | Jung, C. | - |
dc.contributor.author | Kang, T. | - |
dc.contributor.author | Kim, H.J. | - |
dc.contributor.author | Kwak, I. | - |
dc.date.accessioned | 2023-03-08T05:10:34Z | - |
dc.date.available | 2023-03-08T05:10:34Z | - |
dc.date.issued | 2022-10 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/61195 | - |
dc.description.abstract | Researchers are employing deep learning (DL) in many fields, and the scope of its application is expanding. However, because understanding the rationale and validity of DL decisions is difficult, a DL model is occasionally called a black-box model. Here, we focus on a DL-based explainable time-series prediction model. We propose a model based on long short-term memory (LSTM) followed by a convolutional neural network (CNN) with a residual connection, referred to as the LSTM-resCNN. In comparison to one-dimensional CNN, bidirectional LSTM, CNN-LSTM, LSTM-CNN, and MTEX-CNN models, the proposed LSTM-resCNN performs best on the three datasets of fine dust (PM2.5), bike-sharing, and bitcoin. Additionally, we tested with Grad-CAM, Integrated Gradients, and Gradients, three gradient-based approaches for the model explainability. These gradient-based techniques combined very well with the LSTM-resCNN model. Variables and time lags that considerably influence the explainable time-series prediction can be identified and visualized using gradients and integrated gradients. Author | - |
dc.format.extent | 14 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Explainable Time-Series Prediction Using a Residual Network and Gradient-Based Methods | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ACCESS.2022.3213926 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.10, pp 108469 - 108482 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000870214500001 | - |
dc.identifier.scopusid | 2-s2.0-85139877696 | - |
dc.citation.endPage | 108482 | - |
dc.citation.startPage | 108469 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 10 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Convolutional neural networks | - |
dc.subject.keywordAuthor | Data mining | - |
dc.subject.keywordAuthor | Data models | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Logic gates | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | Predictive models | - |
dc.subject.keywordAuthor | Recurrent neural networks | - |
dc.subject.keywordAuthor | Time series analysis | - |
dc.subject.keywordAuthor | Time series analysis | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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