LSTM Fully Convolutional Networks for Time Series Classification

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997

1997

Earlier work this paper cites.

G. King and L. Zeng, “Logistic Regression in Rare Events Data,” Political analysis , vol. 9, no. 2, pp. 137–163, 2001

2001

Earlier work this paper cites.

M. W. Kadous, “Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series,” New South Wales, Australia , 2002

2002

Earlier work this paper cites.

J. Lin, E. Keogh, L. Wei, and S. Lonardi, “Experiencing SAX: A Novel Symbolic Representation of Time Series,” Data Mining and Knowledge Discovery , vol. 15, no. 2, pp. 107–144, apr 2007

2007

Earlier work this paper cites.

A. Graves et al. , Supervised Sequence Labelling with Recurrent Neural Networks . Springer, 2012, vol. 385

2012

Earlier work this paper cites.

M. G. Baydogan, G. Runger, and E. Tuv, “A Bag-of-Features Framework to Classify Time Series,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 11, pp. 2796–2802, nov 2013

2013

Earlier work this paper cites.

R. Pascanu, C. Gulcehre, K. Cho, and Y. Bengio, “How to Construct Deep Recurrent Neural Networks,” arXiv preprint arXiv:1312.6026 , 2013

Original

2013

Earlier work this paper cites.

M. Lin, Q. Chen, and S. Yan, “Network in Network,” arXiv preprint arXiv:1312.4400 , 2013

Original

2013

Earlier work this paper cites.

P. Schäfer, “The BOSS is Concerned with Time Series Classification in the Presence of Noise,” Data Mining and Knowledge Discovery , vol. 29, no. 6, pp. 1505–1530, sep 2014

2014

Cited alongside, same era.

J. Lines and A. Bagnall, “Time Series Classification with Ensembles of Elastic Distance Measures,” Data Mining and Knowledge Discovery , vol. 29, no. 3, pp. 565–592, jun 2014

2014

Cited alongside, same era.

D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” arXiv preprint arXiv:1409.0473 , 2014

Original

2014

Cited alongside, same era.

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting.” Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014

2014

Cited alongside, same era.

S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” in International Conference on Machine Learning , 2015, pp. 448–456

2015

Later among the works it cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Delving Deep into Rectifiers: Surpassing Human-Level Performance on Imagenet Classification,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1026–1034

2015

Later among the works it cites.

P. Schäfer, “Scalable Time Series Classification,” Data Mining and Knowledge Discovery , vol. 30, no. 5, pp. 1273–1298, 2016

2016

Later among the works it cites.

Z. Cui, W. Chen, and Y. Chen, “Multi-Scale Convolutional Neural Networks for Time Series Classification,” arXiv preprint arXiv:1603.06995 , 2016

Original

2016

Later among the works it cites.