A new approach for the implementation of variogram models and ordinary kriging using the R statistical language, in conjunction with Fortran, the MPI （Message Passing Interface）, and the ＂pbdDMAT＂ package within R...A new approach for the implementation of variogram models and ordinary kriging using the R statistical language, in conjunction with Fortran, the MPI （Message Passing Interface）, and the ＂pbdDMAT＂ package within R on the Bridges and Stampede Supercomputers will be described. This new technique has led to great improvements in timing as compared to those in R alone, or R with C and MPI. These improvements include processing and forecasting vectors of size 25,000 in an average time of 6 minutes on the Stampede Supercomputer and 2.5 minutes on the Bridges Supercomputer as compared to previous processing times of 3.5 hours.展开更多
With the benefits of increased computing power and much improved software,temporal disaggregation is examined.Disaggregation,the process of obtaining high frequency data from low frequency data has been discussed for ...With the benefits of increased computing power and much improved software,temporal disaggregation is examined.Disaggregation,the process of obtaining high frequency data from low frequency data has been discussed for many years.This study examines three methods which utilize the autoregressive integrated moving average(ARIMA)model in a simulation study comparing parameter estimation,disaggregation mean square error,and forecast mean square error.Finally,the three methods are applied to a real-world time series.展开更多
文摘A new approach for the implementation of variogram models and ordinary kriging using the R statistical language, in conjunction with Fortran, the MPI （Message Passing Interface）, and the ＂pbdDMAT＂ package within R on the Bridges and Stampede Supercomputers will be described. This new technique has led to great improvements in timing as compared to those in R alone, or R with C and MPI. These improvements include processing and forecasting vectors of size 25,000 in an average time of 6 minutes on the Stampede Supercomputer and 2.5 minutes on the Bridges Supercomputer as compared to previous processing times of 3.5 hours.
文摘With the benefits of increased computing power and much improved software,temporal disaggregation is examined.Disaggregation,the process of obtaining high frequency data from low frequency data has been discussed for many years.This study examines three methods which utilize the autoregressive integrated moving average(ARIMA)model in a simulation study comparing parameter estimation,disaggregation mean square error,and forecast mean square error.Finally,the three methods are applied to a real-world time series.
