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The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades.
Hypothesis Testing in Time Series Analysis
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Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
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Backpropagation through time: what it does and how to do it
Paul J Werbos · 1990
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Distributed representations, simple recurrent networks, and grammatical structure
Jeffrey L Elman · 1991
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Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi · 1994
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Learning long-term dependencies in NARX recurrent neural networks
Tsungnan Lin, Bill G. Horne, Peter Tino, and C. Lee Giles · 1996
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Experience with selecting exemplars from clean data
Mark Plutowski, Garrison Cottrell, and Halbert White · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Narmax time series model prediction: feedforward and recurrent fuzzy neural network approaches
Yang Gao and Meng Joo Er · 2005
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Ensemble learning for time series prediction
Abdelhamid Bouchachia and Saliha Bouchachia · 2008
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Narx-based nonlinear system identification using orthogonal least squares basis hunting
S. Chen, X. X. Wang, and C. J. Harris · 2008
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The use of NARX neural networks to predict chaotic time series
Eugen Diaconescu · 2008
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Time Series: Theory and Methods (2nd ed.)
Peter J. Brockwell and Richard A Davis · 2009
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A dual-stage two-phase model of selective attention
Ronald Hübner, Marco Steinhauser, and Carola Lehle · 2010
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Arima models and the box-jenkins methodology
Dimitros Asteriou and Stephen G Hall · 2011
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Fine-grained photovoltaic output prediction using a bayesian ensemble
Prithwish Chakraborty, Manish Marwah, Martin F Arlitt, and Naren Ramakrishnan · 2012
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Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
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Recurrent continuous translation models
Nal Kalchbrenner and Phil Blunsom · 2013
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Tensorflow: Large-scale machine learning on heterogeneous systems
Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2015
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Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Fei-Fei Li · 2015
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A regularized linear dynamical system framework for multivariate time series analysis
Zitao Liu and Milos Hauskrecht · 2015
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Saliency detection via cellular automata
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Dynamic covariance models for multivariate financial time series
Yue Wu, José Miguel Hernández-Lobato, and Zoubin Ghahramani · 2013
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Substructure vibration NARX neural network approach for statistical damage inference
Linjun Yan, Ahmed Elgamal, and Garrison W. Cottrell · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Integrated pre-processing for bayesian nonlinear system identification with gaussian processes
R. Frigola and C. E. Rasmussen · 2014
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Yao Qin, Huchuan Lu, Yiqun Xu, and He Wang · 2015
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Top rank supervised binary coding for visual search
Dongjin Song, Wei Liu, Rongrong Ji, David A Meyer, and John R Smith · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C Courville, Ruslan Salakhutdinov, Richard S Zemel, and Yoshua Bengio · 2015
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Adaptive feature abstraction for translating video to language
Yunchen Pu, Martin Renqiang Min, Zhe Gan, and Lawrence Carin · 2016
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Fast structural binary coding
Dongjin Song, Wei Liu, and David A Meyer · 2016
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Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy · 2016
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