Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
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Guokun Lai | Yiming Yang | Hanxiao Liu | Wei-Cheng Chang | Hanxiao Liu | Yiming Yang | Wei-Cheng Chang | Guokun Lai
[1] Jürgen Schmidhuber,et al. Highway Networks , 2015, ArXiv.
[2] Deng Cai,et al. What to Do Next: Modeling User Behaviors by Time-LSTM , 2017, IJCAI.
[3] Les E. Atlas,et al. Recurrent Networks and NARMA Modeling , 1991, NIPS.
[4] A. Banerjee,et al. Estimating Structured Vector Autoregressive Models , 2016, ICML.
[5] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[6] S Roberts,et al. Gaussian processes for time-series modelling , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[7] Ugur Demiryurek,et al. Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting , 2017, SDM.
[8] Kyoung-jae Kim,et al. Financial time series forecasting using support vector machines , 2003, Neurocomputing.
[9] Shih-Chii Liu,et al. Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences , 2016, NIPS.
[10] Xiaoli Li,et al. Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition , 2015, IJCAI.
[11] Fang Han,et al. Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes , 2015, ICML.
[12] Roger Frigola,et al. Bayesian Time Series Learning with Gaussian Processes , 2015 .
[13] Jonathan D. Cryer,et al. Time Series Analysis , 1986 .
[14] Carl E. Rasmussen,et al. Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC , 2013, NIPS.
[15] Helmut Ltkepohl,et al. New Introduction to Multiple Time Series Analysis , 2007 .
[16] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1971 .
[17] Francis Eng Hock Tay,et al. Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.
[18] P. Young,et al. Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.
[19] Michael Y. Hu,et al. Forecasting with artificial neural networks: The state of the art , 1997 .
[20] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[21] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[22] Tara N. Sainath,et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.
[23] Alexander J. Smola,et al. Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.
[24] Thomas Plötz,et al. Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables , 2016, IJCAI.
[25] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[26] Guoqiang Peter Zhang,et al. Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.
[27] Takayuki Osogami,et al. Nonlinear Dynamic Boltzmann Machines for Time-Series Prediction , 2017, AAAI.
[28] Inderjit S. Dhillon,et al. Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction , 2016, NIPS.
[29] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[30] E. McKenzie. General exponential smoothing and the equivalent arma process , 1984 .
[31] Ashu Jain,et al. Hybrid neural network models for hydrologic time series forecasting , 2007, Appl. Soft Comput..
[32] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[33] Jiahan Li,et al. Forecasting Macroeconomic Time Series: LASSO-Based Approaches and Their Forecast Combinations with Dynamic Factor Models , 2014 .
[34] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[35] G. Box,et al. Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models , 1970 .
[36] Gregory D. Hager,et al. Temporal Convolutional Networks: A Unified Approach to Action Segmentation , 2016, ECCV Workshops.
[37] Yoshua Bengio,et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.
[38] Charles Elkan,et al. Learning to Diagnose with LSTM Recurrent Neural Networks , 2015, ICLR.
[39] Yan Liu,et al. Recurrent Neural Networks for Multivariate Time Series with Missing Values , 2016, Scientific Reports.