SimTs {greenbrown}R Documentation

Simulate surrogate time series

Description

The function simulates a surrogate (artificial) time series based on the defined properties. See Forkel et al. 2013 for a description how time series are simulated with this function.

Usage

SimTs(M = 0.35, Tslope = c(0.002, -0.004), Isd = 0.015, Irange = 0.03, 
    Srange = 0.5, Rsd = 0.05, Rrange = 0.1, breaks = 120, abrupt = TRUE, 
    n = 360, start = c(1982, 1), freq = 12)

Arguments

M

mean of the time series

Tslope

slope of the trend in each time series segment. slope should be a numeric vector. The length of this vector determines the number of segements.

Isd

standard deviation of the annual mean values (inter-annual variability)

Irange

range of the annual mean values (inter-annual variability)

Srange

range of the seasonal cycle (seasonal amplitude)

Rsd

standard deviation of short-term anomalies

Rrange

range of short-term anomalies

breaks

position of the breakpoints in the time series. You should specify one more slope than breakpoint.

abrupt

Should the trend at the breakpoints change abrupt (TRUE) or gradual (FALSE)?

n

length of the time series

start

beginning of the time series (i.e. the time of the first observation). The default is c(1982, 1), i.e. January 1982 which is the usual start date to compute trends on long-term series of satellite observations of NDVI. See ts for further examples.

freq

The frequency of observations. The default is 12 for monthly observations. Use 24 for bi-monthly observations, 365 for daily observations or 1 for annual observations. See ts for further examples.

Value

The function returns multiple time series of class ts with the following components: total time series, mean, trend component, inter-annual variability component, seasonal component, short-term component.

Author(s)

Matthias Forkel <matthias.forkel@geo.tuwien.ac.at> [aut, cre]

References

Forkel, M., N. Carvalhais, J. Verbesselt, M. Mahecha, C. Neigh and M. Reichstein (2013): Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology. - Remote Sensing 5.

See Also

SimTs

Examples

# simulate artificial time series
x <- SimTs(M=0.4, Tslope=0.0008, Isd=0.015, Irange=0.03, Srange=0.5, Rsd=0.05, 
	Rrange=0.1, breaks=NULL, abrupt=TRUE, n=360, start=c(1982, 1), freq=12)
plot(x)

x <- SimTs(M=0.35, Tslope=c(0.002, -0.0015), Isd=0.015, Irange=0.03, Srange=0.5, 
   Rsd=0.05, Rrange=0.1, breaks=120, abrupt=TRUE, n=360, start=c(1982, 1), freq=12)
plot(x)

x <- SimTs(M=0.4, Tslope=c(0.003, -0.001, 0), Isd=0.03, Irange=0.08, Srange=0.3, 
   Rsd=0.06, 	Rrange=0.2, breaks=c(100, 210), abrupt=FALSE, 
   n=360, start=c(1982, 1), freq=12)
plot(x)


[Package greenbrown version 2.4.3 Index]