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Self-attention Convolutional Autoencoder와 Temporal Convolutional Network를 이용한 Two Phase 다변량 시계열 예측Two Phase Multivariate to Multivariate Time Series Forecasting Using Self-attention Convolutional Autoencoder and Temporal Convolutional Network

Other Titles
Two Phase Multivariate to Multivariate Time Series Forecasting Using Self-attention Convolutional Autoencoder and Temporal Convolutional Network
Authors
황우영백준걸
Issue Date
2022
Publisher
대한산업공학회
Keywords
Cause Analysis; Inter-Relationship; Intra-Relationship; Multivariate to Multivariate Time Series Forecasting; Self-Attention Convolutional Autoencoder; Temporal Convolutional Network
Citation
대한산업공학회지, v.48, no.4, pp.355 - 366
Indexed
KCI
Journal Title
대한산업공학회지
Volume
48
Number
4
Start Page
355
End Page
366
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/143908
ISSN
1225-0988
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.
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공과대학 (산업경영공학부)
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