GPoM (version 1.4)

concat: Concat Concatenates separated time series

Description

The aim of this code is to provide, from a set of multiple time series, a single concatenated time series for applying the global modeling technique to all the time time series in association.

Usage

concat(svrlTS, winL = 9)

Value

concaTS The concatenated time series.

Arguments

svrlTS

All separated time series.

winL

Total number of points used for computing the derivatives of the input time series. This parameter will be used as an input in function drvSucc to compute the derivatives.

Author

Sylvain Mangiarotti, Mireille Huc

References

S. Mangiarotti, F. Le Jean, M. Huc & C. Letellier, 2016. Global modeling of aggregated and associated chaotic dynamics, Chaos, Solitons & Fractals, 83, 82-96.

Examples

# load data
data("svrlTS")
# Concatenate the data set into a single time series
winL = 55
concaTS <- concat(svrlTS, winL = winL)
# Plot the concatenated time series
plot(concaTS$sglTS$TS[,1], concaTS$sglTS$TS[,2],
     main = 'Concatenated time series',
     xlab = 'Time (concatenated)', ylab = 'y(t)',
     type = 'l', col = 'gray')
lines(concaTS$sglTS$TS[concaTS$sglTS$W == 1,1],
      concaTS$sglTS$TS[concaTS$sglTS$W == 1,2], type = 'p', col = 'green', cex = 0.5)
lines(concaTS$sglTS$TS[concaTS$sglTS$W == 0,1],
      concaTS$sglTS$TS[concaTS$sglTS$W == 0,2], type = 'p', col = 'red', cex = 0.5)
lines(concaTS$sglTS$TS[,1], concaTS$sglTS$W, type = 'l')
# \donttest{
# The concatenated data set can be used for global modelling:
GPout1 <- gPoMo(data = concaTS$sglTS$TS[,2], tin = concaTS$sglTS$TS[,1],
                dMax = 2, nS = 3, winL = winL, weight = concaTS$sglTS$W, show = 1,
                IstepMin = 10, IstepMax = 6000, nPmin = 11, nPmax = 11, method = 'rk4')
# }