0000000000308115
AUTHOR
Pd Sampson
showing 2 related works from this author
A spatio-temporal model based on the SVD to analyze large spatio-temporal datasets
2009
A common problem in the analysis of space-time data is to compress a large dataset in order to extract the underlying trends. Empirical orthogonal function (EOF) analysis is a useful tool for examining both the temporal and the spatial variation in atmospherical and physical process and a convenient method of performing this is the Singular Value Decomposition (SVD). Many spatio-temporal models for measurements Z(s; t) at location s at time t, can be written as a sum of a systematic component and a residual component: Z = M+E, where Z, M and E are all T x N matrices. Our approach permits modeling of incomplete data matrices using an "EM-like" iterative algorithm for the SVD. We model the tr…
Dimensionality reduction for large spatio-temporal datasets based on SVD
2009
Many models for spatio-temporal measurements Z(s; t) can be written as a sum of a systematic component and a residual component: Z = M + E. The approach presented here incorporates two Singular Value Decompositions (SVD). The first SVD is applied to the space-time data matrix Z with cross-validation to choose the number of smoothed singular vectors to use as temporal basis functions for modelling spatially varying temporal trend in the matrix M. The second SVD is applied to the spatio-temporal matrix E of residuals from the trend models fitted at each site; it represents spatially correlated short time scale temporal processes. The remaining stochastic structure is explained by simple autor…