Correlational graph attention-based Long Short-Term Memory network for multivariate time series prediction

[1]  Yu Zheng,et al.  GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction , 2018, IJCAI.

[2]  Yixin Cao,et al.  KGAT: Knowledge Graph Attention Network for Recommendation , 2019, KDD.

[3]  Ah Chung Tsoi,et al.  Graph neural networks for ranking Web pages , 2005, The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).

[4]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[5]  Hongwei Zhu,et al.  CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets , 2019, IJCAI.

[6]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[7]  Tianqi Zhang,et al.  Graph Attention LSTM: A Spatiotemporal Approach for Traffic Flow Forecasting , 2020, IEEE Intelligent Transportation Systems Magazine.

[8]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[9]  Guokun Lai,et al.  Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks , 2017, SIGIR.

[10]  Qingkui Chen,et al.  STMAG: A spatial-temporal mixed attention graph-based convolution model for multi-data flow safety prediction , 2020, Inf. Sci..

[11]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[12]  Shufeng Sun,et al.  Attention-Based Long Short-Term Memory Method for Alarm Root-Cause Diagnosis in Chemical Processes , 2020 .

[13]  Jun Hu,et al.  Multistage attention network for multivariate time series prediction , 2020, Neurocomputing.

[14]  Ling Yang,et al.  DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction , 2019, Expert Syst. Appl..

[15]  Hung-yi Lee,et al.  Temporal pattern attention for multivariate time series forecasting , 2018, Machine Learning.

[16]  Yoshua Bengio,et al.  Gated Feedback Recurrent Neural Networks , 2015, ICML.

[17]  Dazhong Wu,et al.  An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction , 2017, Reliab. Eng. Syst. Saf..

[18]  Xiaofei Zhou,et al.  DAN: Deep Attention Neural Network for News Recommendation , 2019, AAAI.

[19]  Garrison W. Cottrell,et al.  A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction , 2017, IJCAI.

[20]  Yanfang Ye,et al.  Heterogeneous Graph Attention Network , 2019, WWW.

[21]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[22]  Rodrigo de Medrano,et al.  A Spatio-Temporal Spot-Forecasting Framework forUrban Traffic Prediction , 2020, ArXiv.

[23]  Amelia Regan,et al.  A spatio-temporal decomposition based deep neural network for time series forecasting , 2020, Appl. Soft Comput..

[24]  Yong Zhang,et al.  Hierarchical Inter-Attention Network for Document Classification with Multi-Task Learning , 2019, IJCAI.

[25]  Akhand Rai,et al.  An integrated approach to bearing prognostics based on EEMD-multi feature extraction, Gaussian mixture models and Jensen-Rényi divergence , 2018, Appl. Soft Comput..

[26]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.

[27]  Enrico Zio,et al.  A Novel Dynamic-Weighted Probabilistic Support Vector Regression-Based Ensemble for Prognostics of Time Series Data , 2015, IEEE Transactions on Reliability.

[28]  Ge Guo,et al.  Short-term traffic speed forecasting based on graph attention temporal convolutional networks , 2020, Neurocomputing.

[29]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[30]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.