Disagg_SPARTA: Disaggregation of a coarse level timeseries sequence to a...

In itsoukal/anySim: Stochastic Simulation of Processes with any Marginal Distribution and Correlation Structure

Description

Disaggregation of a coarse level timeseries sequence to a finer level timeseries sequence exhibiting the target marginal distribution and correlation structure (stationary).

Usage

Disagg_SPARTA(HLSeries, SPARTApar, max.iter, steps, Adjust = T)

Arguments

HLSeries	A vector, thati is coarse level timeseries sequence specifying the values to disaggreate into a time series sequence of finer level..
max.iter	A scalar specifying the maximum number of allowed repetitions (parameter of the disaggregation algorithm - typically set between 300-500.).
steps	A scalar specifying the number of timesteps of the sequence to generate.
Adjust	A logical operator (TRUE or FALSE) specifying whether (TRUE) or not (FALSE) to perfom the proportianal adjusting operation (parameter of the disaggregation algorithm - typically set to TRUE).
ARTApar	A list containing the parameters of the model. The list is constructed by the function "EstARTAp".

Value

A list of the 3 generated time series (in vector format): X: The final time series at the actual domain with the target marginal distribution and correlation structure;

Examples

## Disaggregation of a sequence of 100 steps of annual streamflow to monthly amounts
## The lower-level process (i.e., that of monthly time step) is a cyclostationary one,
while the coarser-level process is stationary. 
## Not run: 

## Simulation of coarser-level (Annual) stationary process ##

# Define the target autocorrelation structure of coarser-level process.
ACS_annual=csCAS(param=c(2.623,1.557),lag=200)

# Define the target distribution function of coarser-level process.
# Here, a re-parameterized version of Gen. Gamma distribution is used.
qgengamma=function(p,scale,shape1,shape2){
  require(VGAM)
  X=qgengamma.stacy(p=p,scale=scale,k=(shape1/shape2),d=shape2)
  return(X)
} 

FX='qgengamma'

# Define the parameters of the target distribution.
pFX=list(scale=7.419,shape1=20.493,shape2=1.198)

# Estimate the parameters of the auxiliary Gaussian AR(p) model.
ARTApar=EstARTAp(ACF=ACS_annual,dist=FX,params=pFX,NatafIntMethod='GH')

# Generate the annual synthetic series of 10000 length. 
simAnnual=SimARTAp(ARTApar = ARTApar, steps = 10^3)

## Simulation of lower-level (Monthly) cyclostationary process ##

# Define the number of seasons.
NumOfSeasons=12 # number of months 

# Define the lag-1 season-to-season correlation coefficients (12 values) of monthly Nile Streamflow.
rtarget_mon=c(0.938,0.931,0.926,0.903,0.761,0.837,0.355,0.662,0.796,0.876,0.826,0.720)

# Define the target distribution functions for each season.
# In this example, the above re-parameterized version of Gen. Gamma distribution is used.
# Or, a re-parameterized version of Burr Type XII distribution.
qburr=function(p,scale,shape1,shape2) {
  require(ExtDist)
  x=ExtDist::qBurr(p=p,b=scale,g=shape1,s=shape2)
  return(x)
}

FXs=c('qgengamma','qburr','qburr','qburr','qburr','qgengamma','qgengamma',
      'qgengamma','qgengamma','qgengamma','qgengamma','qgengamma')

# Define the parameters of distribution functions for each season.
PFXs<-vector("list",NumOfSeasons)
PFXs[[1]]=list(scale=0.000862254,shape1=18.24168,shape2=0.4491688)
PFXs[[2]]=list(scale=2.352517,shape1=6.233872,shape2=0.7284742)
PFXs[[3]]=list(scale=1.586728,shape1=9.007934,shape2=0.4096283)
PFXs[[4]]=list(scale=1.337449,shape1=12.01606,shape2=0.3374601)
PFXs[[5]]=list(scale=1.56249,shape1=6.386645,shape2=0.8020387)
PFXs[[6]]=list(scale=0.0005479373,shape1=18.54147,shape2=0.4500553)
PFXs[[7]]=list(scale=0.001297873,shape1=19.83979,shape2=0.4629369)
PFXs[[8]]=list(scale=15.27454,shape1=5.607777,shape2=3.654064)
PFXs[[9]]=list(scale=17.18964,shape1=7.913649,shape2=3.848175)
PFXs[[10]]=list(scale=8.327586,shape1=7.307034,shape2=2.280058)
PFXs[[11]]=list(scale=9.226506,shape1=2.42338,shape2=4.200226)
PFXs[[12]]=list(scale=0.002727125,shape1=14.18116,shape2=0.4648454)


# Estimate the parameters of SPARTA model.
SPARTApar<-EstSPARTA(s2srtarget=rtarget_mon,dist=FXs,params=PFXs,
                     NatafIntMethod='GH',NoEval=9,polydeg=8,nodes=11)

# Disaggregate the annual series to monthly amounts.
disagMonthly<-Disagg_SPARTA(HLSeries=simAnnual$X[1:100],SPARTApar=SPARTApar,max.iter=300,steps=NumOfSeasons,Adjust=T)

## End(Not run)

itsoukal/anySim documentation built on May 7, 2020, 11:57 p.m.