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 autoregressive models fit to the final residuals. The procedure is applied to 30 years of daily temperature data from Sicily.

Onorati, R., Sampson, P.D., Guttorp, P. (2009). Dimensionality reduction for large spatio-temporal datasets based on SVD. In Statistical methods for the analysis of large data-sets (pp.487-490). Padova : CLEUP.

Dimensionality reduction for large spatio-temporal datasets based on SVD

ONORATI, Rossella;
2009-01-01

Abstract

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 autoregressive models fit to the final residuals. The procedure is applied to 30 years of daily temperature data from Sicily.
2009
Statistical methods for the analysis of large data‐sets
Chieti-Pescara
23-25 September 2009
2009
4
Onorati, R., Sampson, P.D., Guttorp, P. (2009). Dimensionality reduction for large spatio-temporal datasets based on SVD. In Statistical methods for the analysis of large data-sets (pp.487-490). Padova : CLEUP.
Proceedings (atti dei congressi)
Onorati, R; Sampson, PD; Guttorp, P
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/51878
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact