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Spatio-temporal problems are ubiquitous and of vital importance in many research fields.
A. C. Harvey, “Estimating regression models with multiplicative heteroscedasticity,” Econometrica: Journal of the Econometric Society , pp. 461–465, 1976
1976
Earlier work this paper cites.
R. Koenker and G. Bassett Jr, “Regression quantiles,” Econometrica: journal of the Econometric Society , pp. 33–50, 1978
1978
Earlier work this paper cites.
B. W. Silverman, “Some aspects of the spline smoothing approach to non-parametric regression curve fitting,” Journal of the Royal Statistical Society. Series B (Methodological) , pp. 1–52, 1985
1985
Earlier work this paper cites.
T. Bollerslev, R. F. Engle, and D. B. Nelson, “Arch models,” Handbook of econometrics , vol. 4, pp. 2959–3038, 1994
1994
Earlier work this paper cites.
X. He, “Quantile curves without crossing,” The American Statistician , vol. 51, no. 2, pp. 186–192, 1997
1997
Earlier work this paper cites.
R. Koenker, Quantile Regression . Cambridge University Press, 2005
2005
Earlier work this paper cites.
R. Koenker, Quantile Regression , ser. Econometric Society Monographs. Cambridge University Press, 2005
2005
Earlier work this paper cites.
I. Takeuchi, Q. V. Le, T. D. Sears, and A. J. Smola, “Nonparametric quantile estimation,” Journal of Machine Learning Research , vol. 7, no. Jul, pp. 1231–1264, 2006
2006
Earlier work this paper cites.
Y.-H. Wang, “Nonlinear neural network forecasting model for stock index option price: Hybrid gjr–garch approach,” Expert Systems with Applications , vol. 36, no. 1, pp. 564–570, 2009
2009
Earlier work this paper cites.
V. Chernozhukov, I. Fernández-Val, and A. Galichon, “Quantile and probability curves without crossing,” Econometrica , vol. 78, no. 3, pp. 1093–1125, 2010
2010
Earlier work this paper cites.
H. D. Bondell, B. J. Reich, and H. Wang, “Noncrossing quantile regression curve estimation,” Biometrika , vol. 97, no. 4, pp. 825–838, 2010
2010
Earlier work this paper cites.
M. Lázaro-gredilla and M. K. Titsias, “Variational heteroscedastic gaussian process regression,” in In 28th International Conference on Machine Learning (ICML-11) . ACM, 2011, pp. 841–848
2011
Earlier work this paper cites.
E. Mazloumi, G. Rose, G. Currie, and S. Moridpour, “Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction,” Engineering Applications of Artificial Intelligence , vol. 24, no. 3, pp. 534–542, 2011
2011
Cited alongside, same era.
A. Khosravi, E. Mazloumi, S. Nahavandi, D. Creighton, and J. Van Lint, “Prediction intervals to account for uncertainties in travel time prediction,” Intelligent Transportation Systems, IEEE Transactions on , vol. 12, no. 2, pp. 537–547, 2011
2011
Cited alongside, same era.
A. Khosravi, E. Mazloumi, S. Nahavandi, D. Creighton, and V. Lint, “A genetic algorithm-based method for improving quality of travel time prediction intervals,” Transportation Research Part C: Emerging Technologies , vol. 19, no. 6, pp. 1364–1376, 2011
2011
Cited alongside, same era.
A. Khosravi, S. Nahavandi, D. Creighton, and A. F. Atiya, “Comprehensive review of neural network-based prediction intervals and new advances,” Neural Networks, IEEE Transactions on , vol. 22, no. 9, pp. 1341–1356, 2011
F. Chollet, “Keras,” https://github.com/fchollet/keras , 2015
2015
Later among the works it cites.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning , 2016, pp. 1050–1059
2016
Later among the works it cites.
M. Sangnier, O. Fercoq, and F. d’Alché Buc, “Joint quantile regression in vector-valued rkhss,” in Advances in Neural Information Processing Systems , 2016, pp. 3693–3701
2016
Later among the works it cites.
Y. Gal, “Uncertainty in deep learning,” University of Cambridge , 2016
2016
Later among the works it cites.
J. Zhang, Y. Zheng, and D. Qi, “Deep spatio-temporal residual networks for citywide crowd flows prediction.” in AAAI , 2017, pp. 1655–1661
2017
Later among the works it cites.
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2011
Cited alongside, same era.
S. K. Schnabel and P. H. Eilers, “Simultaneous estimation of quantile curves using quantile sheets,” AStA Advances in Statistical Analysis , vol. 97, no. 1, pp. 77–87, 2013
2013
Cited alongside, same era.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural networks , vol. 61, pp. 85–117, 2015
2015
Cited alongside, same era.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Cited alongside, same era.
S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional lstm network: A machine learning approach for precipitation nowcasting,” in Advances in neural information processing systems , 2015, pp. 802–810
2015
Cited alongside, same era.
N. Srivastava, E. Mansimov, and R. Salakhudinov, “Unsupervised learning of video representations using lstms,” in International conference on machine learning , 2015, pp. 843–852
2015
Cited alongside, same era.
2015
Cited alongside, same era.
H.-W. Kang and H.-B. Kang, “Prediction of crime occurrence from multi-modal data using deep learning,” PloS one , vol. 12, no. 4, p. e0176244, 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
P.-Y. Hao, “Pair-{ v v }-svr: A novel and efficient pairing nu-support vector regression algorithm,” IEEE transactions on neural networks and learning systems , vol. 28, no. 11, pp. 2503–2515, 2017
2017
Later among the works it cites.
F. Antunes, A. O’Sullivan, F. Rodrigues, and F. Pereira, “A review of heteroscedasticity treatment with gaussian processes and quantile regression meta-models,” in Seeing Cities Through Big Data . Springer, 2017, pp. 141–160
2017
Later among the works it cites.
New York City Taxi & Limousine Commission, “Taxi and limousine commission (tlc) trip record data,” 2017, Available: http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml
2017
Later among the works it cites.
2017
Later among the works it cites.
Y. Yang, S. Li, W. Li, and M. Qu, “Power load probability density forecasting using gaussian process quantile regression,” Applied Energy , vol. 213, pp. 499–509, 2018
2018
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