Spatiotemporal System Identification With Continuous Spatial Maps and Sparse Estimation.
Aram, P.; Kadirkamanathan, V.; Anderson, S.R.;
Publisher: Institute of Electrical and Electronics Engineers
We present a framework for the identification of spatiotemporal linear dynamical systems. We use a state-space model representation that has the following attributes: 1) the number of spatial observation locations are decoupled from the model order; 2) the model allows ... View more
 C. K. Wikle, R. Madden, and T. Chen, “Seasonal variation of upper tropospheric and lower stratospheric equatorial waves over the tropical Pacific,” Journal of the Atmospheric Sciences, vol. 54, no. 14, pp. 1895- 1909, 1997.
 K. E. Hild, H. T. Attias, and S. S. Nagarajan, “An expectationmaximization method for spatio-temporal blind source separation using an AR-MOG source model,” IEEE Trans. Neural Networks, vol. 19, pp. 508-519, Mar. 2008.
 P. Aram, D. Freestone, M. Dewar, K. Scerri, V. Jirsa, D. Grayden, and V. Kadirkamanathan, “Spatiotemporal multi-resolution approximation of the Amari type neural field model,” NeuroImage, vol. 66, pp. 88-102, 2012.
 L. A. Waller, B. P. Carlin, H. Xia, and A. E. Gelfand, “Hierarchical spatio-temporal mapping of disease rates,” Journal of the American Statistical Association, vol. 92, no. 438, pp. 607-617, 1997.
 D. Gu and H. Hu, “Spatial Gaussian process regression with mobile sensor networks,” IEEE Trans. Neural Networks and Learning Systems, vol. 23, pp. 1279-1290, Aug. 2012.
 N. Cressie and C. K. Wikle, Statistics for Spatio-Temporal Data. Hoboken, New Jersey: Wiley, 2011.
 P. E. Pfeifer and S. J. Deutsch, “Independence and sphericity tests for the residuals of space-time arma models,” Communications in Statistics - Simulation and Computation, vol. 9, no. 5, pp. 533-549, 1980.
 D. S. Stoffer, “Estimation and identification of space-time ARMAX models in the presence of missing data,” Journal of the American Statistical Association, vol. 81, no. 395, pp. 762-772, 1986.
 J. R. Stroud, P. Muller, and B. Sanso, “Dynamic models for spatiotemporal data,” Journal of the Royal Statistical Society. Series B, vol. 63, no. 4, pp. 673-689, 2001.
 C. K. Wikle, “A kernel-based spectral approach for spatio-temporal dynamic models,” in Proceedings of the 1st Spanish Workshop on SpatioTemporal Modelling of Environmental Processes (METMA), Benicassim, Spain , Oct. 2001, pp. 167-180.