[1] CRESSIE N,WIKLE C K.Statistics for spatio-temporal data[M].[S.l.]:John Wiley & Sons,2015. [2] HUANG H C,CRESSIE N.Spatio-temporal prediction of snow water equivalent using the Kalman filter[J].Computational Statistics & Data Analysis,1996,22(2):159-175. [3] CRESSIE N.The origins of kriging[J].Mathematical Geology,1990,22(3):239-252. [4] JONES R H,ZHANG Y M.Models for continuous stationary space-time processes[M]//GREGOIRE T G.Modelling longitudinal and spatially correlated data.Berlin,Germany:Springer,1997:289-298. [5] MONTERO J,FERNÁNDEZ-AVILÉS G,MATEU J.Spatial and spatio-temporal geostatistical modeling and kriging[M].[S.l.]:John Wiley & Sons,2015. [6] HOOPER P M,HEWINGS G J D.Some properties of space-time processes[J].Geographical Analysis,2010,13(3):203-223. [7] OLIVER M A,WEBSTER R.Kriging:a method of interpolation for geographical information systems[J].International Journal of Geographical Information Systems,1990,4(3):313-332. [8] CRESSIE N,JOHANNESSON G.Fixed rank kriging for very large spatial data sets[J].Journal of the Royal Statistical Society:Series B(Statistical Methodology),2008,70(1):209-226. [9] CRESSIE N,SHI T,KANG E L.Fixed rank filtering for spatio-temporal data[J].Journal of Computational and Graphical Statistics,2010,19(3):724-745. [10] NGUYEN H,KATZFUSS M,CRESSIE N,et al.Spatio-temporal data fusion for very large remote sensing datasets[J].Technometrics,2014,56(2):174-185. [11] CLIFF A D,ORD J K.Space-time modelling with an application to regional forecasting[J].Transactions of the Institute of British Geographers,1975(64):119-120. [12] MARTIN R L,OEPPEN J E.The identification of regional forecasting models using space:time correlation functions[J].Transactions of the Institute of British Geographers,1975(66):95-98. [13] PATRICK J D,HARVILL J L,HANSEN C W.A semiparametric spatio-temporal model for solar irradiance data[J].Renewable Energy,2016,87:15-30. [14] ANDRÉ M,DABO-NIANG S,SOUBDHAN T,et al.Predictive spatio-temporal model for spatially sparse global solar radiation data[J].Energy,2016,111:599-608. [15] ZHAO Youlin,GE Liang,ZHOU Yijun,et al.A new seasonal difference space-time autoregressive integrated moving average model and spatiotemporal trend prediction analysis for hemorrhagic fever with renal syndrome[J].PLoS One,2018,13(11):518-526. [16] BESSA R J,TRINDADE A,MIRANDA V.Spatial-temporal solar power forecasting for smart grids[J].IEEE Transactions on Industrial Informatics,2015,11(1):232-241. [17] MESSNER J W,PINSON P.Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting[J].International Journal of Forecasting,2019,35(4):1485-1498. [18] BAHADORI M T,YU Q R,LIU Y.Fast multivariate spatio-temporal analysis via low rank tensor learning[C]//Proceedings of Advances in Neural Information Processing Systems.Washington D.C.,USA:IEEE Press,2014:3491-3499. [19] BAROCIO E,PAL B C,THORNHILL N F,et al.A dynamic mode decomposition framework for global power system oscillation analysis[J].IEEE Transactions on Power Systems,2015,30(6):2902-2912. [20] LI Yexin,ZHENG Yu,ZHANG Huichu,et al.Traffic prediction in a bike-sharing system[C]//Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems.New York,USA:ACM Press,2015:33-39. [21] SHI Xingjian,CHEN Zhourong,WANG Hao,et al.Convolutional LSTM network:a machine learning approach for precipitation nowcasting[EB/OL].[2019-11-10].https://arxiv.org/abs/1506.04214. [22] CHE Z P,PURUSHOTHAM S,CHO K,et al.Recurrent neural networks for multivariate time series with missing values[J].Scientific Reports,2018,8:6085-6090. [23] ZHANG J B,ZHENG Y,QI D K.Deep spatio-temporal residual networks for citywide crowd flows prediction[EB/OL].[2019-11-10].https://arxiv.org/abs/1610.00081. [24] HOWARD J P.Data-driven modeling & scientific computation:methods for complex systems & big data[EB/OL].[2019-11-10].https://www.amazon.com/Data-Driven-Modeling-Scientific-Computation-Methods/dp/0199660344. [25] BROCKWELL P J,DAVIS R A.Forecasting techniques[M]//SHANMUGAM R.Introduction to time series and forecasting.Berlin,Germany:Springer,2016:309-321. [26] BURNHAM K P,ANDERSON D R.Multimodel inference[J].Sociological Methods & Research,2004,33(2):261-304. [27] WILLMOTT C J,MATSUURA K.Advantages of the mean absolute error over the root mean square error in assessing average model performance[J].Climate Research,2005,30:79-82. [28] RUBIN D B.The Bayesian Bootstrap[J].The Annals of Statistics,1981,9(1):130-134. |