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연속적인 시계열 예측을 위한 디노이징 다변량 시계열 모델링
홍정수(Jungsoo Hong),박진욱(Jinuk Park),이지은(Jieun Lee),김경훈(Kyeonghun Kim),홍승균(Seung-Kyun Hong),박상현(Sanghyun Park) Korean Institute of Information Scientists and Eng 2021 정보과학회논문지 Vol.48 No.8
The research field of time series forecasting predicts the future time point using seasonality in time series. In the industrial environment, since decision-making through continuous perspective prediction of the future is important, multi-step time series forecasting is necessary. However, multi-step prediction is highly unstable because of its dependency on predicted value of previous time prediction result. Therefore, the traditional time series forecasting makes a statistical prediction for the single time point. To address this limitation, we propose a novel encoder-decoder based neural network named ‘DTSNet’ which predicts multi-step time points for multivariate time series. To stabilize multi-step prediction, we exploit positional encoding to enhance representation for time point and propose a novel denoising training method. Moreover, we propose dual attention to resolve long-term dependencies and modeling complex patterns in time series, and we adopt multi-head strategy at linear projection layer for variable-specific modeling. To verify the performance improvement of our approach, we compare and analyze it with baseline models, and we demonstrate the proposed methods through comparison tests, such as, component ablation study and denoising degree experiment.