Look Ahead: Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features
DC Field | Value | Language |
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dc.contributor.author | Kim, Seonmin | - |
dc.contributor.author | Chae, Dong-Kyu | - |
dc.date.accessioned | 2023-11-24T04:49:26Z | - |
dc.date.available | 2023-11-24T04:49:26Z | - |
dc.date.created | 2023-09-04 | - |
dc.date.issued | 2023-07 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/192946 | - |
dc.description.abstract | Time-series forecasting has been actively studied and adopted in various real-world domains. Recently there have been two research mainstreams in this area: building Transformer-based architectures such as Informer, Autoformer and Reformer, and developing time-series representation learning frameworks based on contrastive learning such as TS2Vec and CoST. Both efforts have greatly improved the performance of time series forecasting. In this paper, we investigate a novel direction towards improving the forecasting performance even more, which is orthogonal to the aforementioned mainstreams as a model-agnostic scheme. We focus on time stamp embeddings that has been less-focused in the literature. Our idea is simple-yet-effective: based on given current time stamp, we predict embeddings of its near future time stamp and utilize the predicted embeddings in the time-series (value) forecasting task. We believe that if such future time information can be previewed at the time of prediction, they can be utilized by any time-series forecasting models as useful additional information. Our experimental results confirmed that our method consistently and significantly improves the accuracy of the recent Transformer-based models and time-series representation learning frameworks. Our code is available at: https://github.com/sunsunmin/Look_Ahead. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | Look Ahead: Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chae, Dong-Kyu | - |
dc.identifier.doi | 10.1145/3539618.3592013 | - |
dc.identifier.scopusid | 2-s2.0-85168651793 | - |
dc.identifier.bibliographicCitation | SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.2134 - 2138 | - |
dc.relation.isPartOf | SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval | - |
dc.citation.title | SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval | - |
dc.citation.startPage | 2134 | - |
dc.citation.endPage | 2138 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Embeddings | - |
dc.subject.keywordPlus | Forecasting | - |
dc.subject.keywordAuthor | Time-series forecasting | - |
dc.subject.keywordAuthor | Time-series representation learning | - |
dc.subject.keywordAuthor | Timestamp embedding | - |
dc.subject.keywordAuthor | Transformer-based architectures | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3539618.3592013 | - |
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