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Over the past decade, multivariate time series classification has received great attention.
P. Nemenyi, Distribution-free multiple comparisons, in: Biometrics, Vol. 18, INTERNATIONAL BIOMETRIC SOC 1441 I ST, NW, SUITE 700, WASHINGTON, DC 20005-2210, 1962, p. 263
1962
Earlier work this paper cites.
Z. Šidák, Rectangular confidence regions for the means of multivariate normal distributions, Journal of the American Statistical Association 62 (318) (1967) 626–633
1967
Earlier work this paper cites.
A. Kehagias, V. Petridis, Predictive modular neural networks for time series classification, Neural Networks 10 (1) (1997) 31–49
1997
Earlier work this paper cites.
S. Hochreiter, J. Schmidhuber, Long Short-Term Memory, Neural computation 9 (8) (1997) 1735–1780
1997
Earlier work this paper cites.
V. Pavlovic, B. J. Frey, T. S. Huang, Time-series classification using mixed-state dynamic bayesian networks, in: Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., Vol. 2, IEEE, 1999, pp. 609–615
1999
Earlier work this paper cites.
T. Jaakkola, M. Diekhans, D. Haussler, A discriminative framework for detecting remote protein homologies, Journal of computational biology 7 (1-2) (2000) 95–114
2000
Earlier work this paper cites.
P. Geurts, Pattern extraction for time series classification, in: PKDD, Vol. 1, Springer, 2001, pp. 115–127
2001
Earlier work this paper cites.
G. King, L. Zeng, Logistic Regression in Rare Events Data, Political analysis 9 (2) (2001) 137–163
2001
Earlier work this paper cites.
A. Graves, J. Schmidhuber, Framewise phoneme classification with bidirectional lstm and other neural network architectures, Neural Networks 18 (5-6) (2005) 602–610
2005
Earlier work this paper cites.
B. Williams, M. Toussaint, A. J. Storkey, Modelling motion primitives and their timing in biologically executed movements, in: Advances in neural information processing systems, 2008, pp. 1609–1616
2008
Earlier work this paper cites.
C. Orsenigo, C. Vercellis, Combining discrete svm and fixed cardinality warping distances for multivariate time series classification, Pattern Recognition 43 (11) (2010) 3787–3794
2010
Earlier work this paper cites.
Z. Xing, J. Pei, E. Keogh, A brief survey on sequence classification, ACM Sigkdd Explorations Newsletter 12 (1) (2010) 40–48
2010
Earlier work this paper cites.
N. Hammami, M. Bedda, Improved tree model for arabic speech recognition, in: Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, Vol. 5, IEEE, 2010, pp. 521–526
2010
Earlier work this paper cites.
W. Li, Z. Zhang, Z. Liu, Action recognition based on a bag of 3d points, in: Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on, IEEE, 2010, pp. 9–14
2010
Cited alongside, same era.
S. Spiegel, J. Gaebler, A. Lommatzsch, E. De Luca, S. Albayrak, Pattern recognition and classification for multivariate time series, in: Proceedings of the fifth international workshop on knowledge discovery from sensor data, ACM, 2011, pp. 34–42
2011
Cited alongside, same era.
L. Maaten, Learning discriminative fisher kernels, in: Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011, pp. 217–224
2011
Cited alongside, same era.
A. Graves, et al., Supervised Sequence Labelling with Recurrent Neural Networks, Vol. 385, Springer, 2012
2012
Cited alongside, same era.
Y. Fu, Human Activity Recognition and Prediction, Springer, 2015
2015
Later among the works it cites.
Z. Yu, M. Lee, Real-time human action classification using a dynamic neural model, Neural Networks 69 (2015) 29–43
2015
Later among the works it cites.
S. Seto, W. Zhang, Y. Zhou, Multivariate time series classification using dynamic time warping template selection for human activity recognition, in: Computational Intelligence, 2015 IEEE Symposium Series on, IEEE, 2015, pp. 1399–1406
2015
Later among the works it cites.
M. G. Baydogan, G. Runger, Learning a symbolic representation for multivariate time series classification, Data Mining and Knowledge Discovery 29 (2) (2015) 400–422
2015
Later among the works it cites.
K. He, X. Zhang, S. Ren, 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
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2012
Cited alongside, same era.
J. Wang, Z. Liu, Y. Wu, J. Yuan, Mining actionlet ensemble for action recognition with depth cameras, in: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, IEEE, 2012, pp. 1290–1297
2012
Cited alongside, same era.
[link] . URL http://www.cs.cmu.edu/~bobski/
R. T. Olszewski (2012) · 2012
Cited alongside, same era.
M. Lichman, UCI machine learning repository (2013). URL http://archive.ics.uci.edu/ml
2013
Cited alongside, same era.
H. Kang, S. Choi, Bayesian common spatial patterns for multi-subject eeg classification, Neural Networks 57 (2014) 39–50
2014
Cited alongside, same era.
Y. Zheng, Q. Liu, E. Chen, Y. Ge, J. L. Zhao, Time series classification using multi-channels deep convolutional neural networks, in: International Conference on Web-Age Information Management, Springer, 2014, pp. 298–310
2014
Cited alongside, same era.
Y. C. Sübakan, B. Kurt, A. T. Cemgil, B. Sankur, Probabilistic sequence clustering with spectral learning, Digital Signal Processing 29 (2014) 1–19
2014
Cited alongside, same era.
Y. Cui, J. Shi, Z. Wang, Complex rotation quantum dynamic neural networks (crqdnn) using complex quantum neuron (cqn): applications to time series prediction, Neural Networks 71 (2015) 11–26
2015
Cited alongside, same era.
2015
Later among the works it cites.
F. Chollet, et al., Keras, https://github.com/fchollet/keras (2015)
2015
Later among the works it cites.
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng, TensorFlow: Large-scale machine learning on heterogeneous systems , software available from tensorflow.org (2015). URL https://www.tensorflow.org/
2015
Later among the works it cites.
L. Wang, Z. Wang, S. Liu, An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm, Expert Systems with Applications 43 (2016) 237–249
2016
Later among the works it cites.
M. G. Baydogan, G. Runger, Time series representation and similarity based on local autopatterns, Data Mining and Knowledge Discovery 30 (2) (2016) 476–509
2016
Later among the works it cites.
F. Karim, S. Majumdar, H. Darabi, S. Chen, Lstm fully convolutional networks for time series classification, IEEE Access (2017) 1–7
2017
Later among the works it cites.
Z. Wang, W. Yan, T. Oates, Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline, in: Neural Networks (IJCNN), 2017 International Joint Conference on, IEEE, 2017, pp. 1578–1585
2017
Later among the works it cites.
K. S. Tuncel, M. G. Baydogan, Autoregressive forests for multivariate time series modeling, Pattern Recognition 73 (2018) 202–215
2018
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