concat: Concat Concatenates separated time series

In GPoM: Generalized Polynomial Modelling

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)

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.

Value

concaTS The concatenated time series.

Author(s)

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')

# 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')

GPoM documentation built on July 9, 2023, 6:23 p.m.