Disagg_ARTAp: 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_ARTAp(HLSeries, ARTApar, 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..
ARTApar	A list containing the parameters of the model. The list is constructed by the function "EstARTAp".
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).

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 500 steps of daily rainfall to 10-minute amounts
## The lower-level process (i.e., that of 10-minute) is a zero-inflated one with p0=0.95, 
## and a Burr Type XII distribution for the continuous part.
## The target autocorrelation strucure is from CAS ACS.
## Not run: 

set.seed(124)

# Define the target autocorrelation structure of the finer-level process.
ACS=csCAS(param=c(1.688,1),lag=24) # CAS with b=1.688 and k=1.

# Define the target distribution function (ICDF).
FX='qmixed'# Define that distribution is of zero-inflated type.

# Define the distribution for the continuous part of the process.
# Here, a re-parameterized version of Burr Type-XII distribution is used.
qburr=function(p,scale,shape1,shape2) {
      require(ExtDist)
      x=ExtDist::qBurr(p=p,b=scale,g=shape1,s=shape2)
      return(x)
}

# Define the parameters of the zero-inflated distribution function.
pFX=list(p0=0.96,Distr=qburr,scale=0.181,shape1=7.642,shape2=0.296)
 
# Estimate the parameters of the auxiliary Gaussian AR(p) model.
param=EstARTAp(ACF=ACS,dist=FX,params=pFX,NatafIntMethod='GH')

# Compose the daily series to be disaggregated
Sim=SimARTAp(ARTApar=param,burn=1000,steps=(24*6*500)) # generation of 10-min series
DailySeries=apply(X=matrix(data=Sim$X,ncol=24*6,byrow=1),MARGIN=1,FUN=sum)

## Disaggregate daily series to 10-min data
disag10min=Disagg_ARTAp(HLSeries=DailySeries,ARTApar=param,max.iter=500,steps=24*6,Adjust=1)

## End(Not run)

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