文章基本信息

标题：A Novel Fusion Framework for Sequential Data using Pre-trained Model
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作者：Tao Ruan ; Canghong Jin ; Lei Xu 等
期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2020
卷号：47
期号：3
语种：English
出版社：IAENG - International Association of Engineers
摘要：Sequential data is widely used in several fields, such as power payload prediction, traffic flow prediction, and stock trend prediction. Driven by the urgent needs, sequential forecasting based on deep learning methods has received lots of attention in recent years. However, the potential of deep learning methods in sequential data forecasting has not yet fully been exploited in terms of model architecture. In this study, a pre-trained nodal model with multiple fusion layers architecture (Sequence Prediction via Node Fusion, SPNF) was proposed, the model considered both connections of nodes and the temporal components of nodes to predict value of next node. Multiple fusion layers were adopted to capture spatial features and temporal dependencies from historical data. The proposed model also conpensated missing data via a masking mechanism. To validate the proposed model, experiments were also carried out using field-captured traffic data, the performance of proposed model were compared with classical and state-of-theart models, such as, ARIMA, SVR, LSTM, TGC-LSTM neural networks. The results showed that proposed model yields higher accuracy and robustness than others, especially in the case of large sequence changes occurred.
关键词：Data Fusion;Sequential Data Prediction;Attention Neural Network;Pre-trained Model