Self-attention Convolutional Autoencoder와 Temporal Convolutional Network를 이용한 Two Phase 다변량 시계열 예측
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 황우영 | - |
dc.contributor.author | 백준걸 | - |
dc.date.accessioned | 2022-09-24T17:40:18Z | - |
dc.date.available | 2022-09-24T17:40:18Z | - |
dc.date.created | 2022-09-23 | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 1225-0988 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/143908 | - |
dc.description.abstract | In manufacturing process, data is collected in the form of correlated sequences. Multivariate to multivariate time series (MMTS) forecasting is an important factor in manufacturing. MMTS forecasting is a notoriously challenging task considering the need for incorporating both non-linear correlations between variables (inter-relationships) and temporal relationships of each univariate time series (intra-relationships) while forecasting future time steps of each univariate time series (UTS) simultaneously. However, previous works use deep learning models suited for low-dimensional data. These models are insufficient to model high-dimensional relationships inherent in multivariate time series (MTS) data. Furthermore, these models are less productive and efficient as they focus on predicting a single target variable from multiple input variables. Thus, we proposed two phase MTS forecasting. First, the proposed method learns the non-linear correlations between UTS (inter-relationship) through self-attention based convolutional autoencoder and conducts cause analysis. Second, it learns the temporal relationships (intra-relationships) of MTS data through temporal convolutional network and forecasts multiple target outputs. As an end-to-end model, the proposed method is more efficient and derives excellent experimental results. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | 대한산업공학회 | - |
dc.title | Self-attention Convolutional Autoencoder와 Temporal Convolutional Network를 이용한 Two Phase 다변량 시계열 예측 | - |
dc.title.alternative | Two Phase Multivariate to Multivariate Time Series Forecasting Using Self-attention Convolutional Autoencoder and Temporal Convolutional Network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 백준걸 | - |
dc.identifier.bibliographicCitation | 대한산업공학회지, v.48, no.4, pp.355 - 366 | - |
dc.relation.isPartOf | 대한산업공학회지 | - |
dc.citation.title | 대한산업공학회지 | - |
dc.citation.volume | 48 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 355 | - |
dc.citation.endPage | 366 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002866178 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Cause Analysis | - |
dc.subject.keywordAuthor | Inter-Relationship | - |
dc.subject.keywordAuthor | Intra-Relationship | - |
dc.subject.keywordAuthor | Multivariate to Multivariate Time Series Forecasting | - |
dc.subject.keywordAuthor | Self-Attention Convolutional Autoencoder | - |
dc.subject.keywordAuthor | Temporal Convolutional Network | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.