generateMTS function

Simulation of multiple time series with given marginals and spatiotemporal properties

Generates multiple time series with given marginals and spatiotemporal properties, just provide (1) the output of fitVAR function, and (2) the number of time steps to simulate.

generateMTS(n, STmodel)

Arguments

• n: number of fields (time steps) to simulate
• STmodel: list of arguments resulting from fitVAR function

Details

Referring to the documentation of fitVAR for details on computational complexity of the fitting algorithm, here we report indicative simulation CPU times for some settings, assuming that the model parameters are already evaluated. CPU times refer to a Windows 10 Pro x64 laptop with Intel(R) Core(TM) i7-6700HQ CPU @ 2.60GHz, 4-core, 8 logical processors, and 32GB RAM.

CPU time:

d = 900, p = 1, n = 1000: ~17s

d = 900, p = 1, n = 10000: ~75s

d = 900, p = 5, n = 100: ~280s

d = 900, p = 5, n = 1000: ~302s

d = 2500, p = 1, n = 1000 : ~160s

d = 2500, p = 1, n = 10000 : ~570s

where $d$ denotes the number of spatial locations

Examples

## Simulation of a 4-dimensional vector with VAR(1) correlation structure
coord <- cbind(runif(4)*30, runif(4)*30)

fit <- fitVAR(
  spacepoints = coord,
  p = 1,
  margdist ='burrXII',
  margarg = list(scale = 3,
                 shape1 = .9,
                 shape2 = .2),
  p0 = 0.8,
  stcsid = "clayton",
  stcsarg = list(scfid = "weibull",
                 tcfid = "weibull",
                 copulaarg = 2,
                 scfarg = list(scale = 20,
                               shape = 0.7),
                 tcfarg = list(scale = 1.1,
                               shape = 0.8))
)

sim <- generateMTS(n = 100,
                     STmodel = fit)