Deep integro-difference equation models for spatio-temporal forecasting
Tài liệu tham khảo
Allaire, 2018
Brynjarsdóttir, 2014, Learning about physical parameters: The importance of model discrepancy, Inverse Problems, 30, 10.1088/0266-5611/30/11/114007
Caines, 2018
Calder, 2011, Modeling space–time dynamics of aerosols using satellite data and atmospheric transport model output, J. Agric. Biol. Environ. Stat., 16, 495, 10.1007/s13253-011-0068-4
Coleman, 2005
Cressie, 1999, Classes of nonseparable, spatio-temporal stationary covariance functions, J. Amer. Statist. Assoc., 94, 1330, 10.1080/01621459.1999.10473885
Cressie, 2011
de Bezenac, E., Pajot, A., Gallinari, P., 2018. Deep learning for physical processes: Incorporating prior scientific knowledge. In: Proceedings of ICLR 2018. Vancouver, Canada.
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der Smagt, P., Cremers, D., Brox, T., 2015. Flownet: Learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile, pp. 2758–2766.
Freestone, 2011, A data-driven framework for neural field modeling, NeuroImage, 56, 1043, 10.1016/j.neuroimage.2011.02.027
Furrer, 2006, Covariance tapering for interpolation of large spatial datasets, J. Comput. Graph. Statist., 15, 502, 10.1198/106186006X132178
Gibson, 2005, Robust maximum-likelihood estimation of multivariable dynamic systems, Automatica, 41, 1667, 10.1016/j.automatica.2005.05.008
Gneiting, 2007, Geostatistical space–time models, stationarity, separability and full symmetry, 151
Gneiting, 2007, Strictly proper scoring rules, prediction, and estimation, J. Amer. Statist. Assoc., 102, 359, 10.1198/016214506000001437
Goodfellow, 2016
Hamilton, 1994
Katzfuss, 2016, Understanding the ensemble Kalman filter, Amer. Statist., 70, 350, 10.1080/00031305.2016.1141709
Katzfuss, 2020, Ensemble Kalman methods for high-dimensional hierarchical dynamic space–time models, J. Amer. Statist. Assoc., 10.1080/01621459.2019.1592753
Kot, 1996, Dispersal data and the spread of invading organisms, Ecology, 77, 2027, 10.2307/2265698
Kot, 1986, Discrete-time growth-dispersal models, Math. Biosci., 80, 109, 10.1016/0025-5564(86)90069-6
Leeds, 2014, Emulator-assisted reduced-rank ecological data assimilation for nonlinear multivariate dynamical spatio-temporal processes, Stat. Methodol., 17, 126, 10.1016/j.stamet.2012.11.004
McDermott, 2017, An ensemble quadratic echo state network for non-linear spatio-temporal forecasting, Stat, 6, 315, 10.1002/sta4.160
McDermott, 2019, Deep echo state networks with uncertainty quantification for spatio-temporal forecasting, Environmetrics, 30, 10.1002/env.2553
Montero, 2015
Nguyen, 2019
Pebesma, 2004, Multivariable geostatistics in S: the gstat package, Comput. Geosci., 30, 683, 10.1016/j.cageo.2004.03.012
R Core Team, 2019
Richardson, 2017, Flexible integro-difference equation modeling for spatio-temporal data, Comput. Statist. Data Anal., 109, 182, 10.1016/j.csda.2016.11.011
Richardson, 2018, Bayesian non-parametric modeling for integro-difference equations, Stat. Comput., 28, 87, 10.1007/s11222-016-9719-1
Shumway, 2006
Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929
Tran, 2020, Bayesian deep net GLM and GLMM, J. Comput. Graph. Statist., 10.1080/10618600.2019.1637747
Wikle, 2002, A kernel-based spectral model for non-Gaussian spatio-temporal processes, Stat. Model., 2, 299, 10.1191/1471082x02st036oa
Wikle, 2019, Comparison of deep neural networks and deep hierarchical models for spatio-temporal data, J. Agric. Biol. Environ. Stat., 24, 175, 10.1007/s13253-019-00361-7
Wikle, 2007, A Bayesian tutorial for data assimilation, Physica D, 230, 1, 10.1016/j.physd.2006.09.017
Wikle, 1999, A dimension-reduced approach to space–time Kalman filtering, Biometrika, 86, 815, 10.1093/biomet/86.4.815
Wikle, 2011, Polynomial nonlinear spatio-temporal integro-difference equation models, J. Time Series Anal., 32, 339, 10.1111/j.1467-9892.2011.00729.x
Wikle, 2010, A general science-based framework for dynamical spatio-temporal models, Test, 19, 417, 10.1007/s11749-010-0209-z
Wikle, 2001, Spatiotemporal hierarchical Bayesian modeling tropical ocean surface winds, J. Amer. Statist. Assoc., 96, 382, 10.1198/016214501753168109
Wikle, 2019
Xu, 2005, A kernel-based spatio-temporal dynamical model for nowcasting radar precipitation, J. Amer. Statist. Assoc., 100, 1133, 10.1198/016214505000000682
Zammit-Mangion, 2016, Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion, Spat. Stat., 18, 194, 10.1016/j.spasta.2016.06.005
Zammit-Mangion, 2015, Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion, Chemometr. Intell. Lab. Syst., 149, 227, 10.1016/j.chemolab.2015.09.006
Zammit-Mangion, 2012, Point process modelling of the Afghan War Diary, Proc. Natl. Acad. Sci., 109, 12414, 10.1073/pnas.1203177109
Zammit-Mangion, 2019
Zammit Mangion, 2011, A variational approach for the online dual estimation of spatiotemporal systems governed by the IDE, IFAC Proc., 44, 3204, 10.3182/20110828-6-IT-1002.02459