ChaoticTimeSeries {fSeries}R Documentation

Chaotic Time Series Modelling

Description

A collection and description of functions to investigate the chaotic behavior of time series processes. Included are functions to simulate different types of chaotic time series maps.

Chaotic Time Series Maps:

henonSim Simulates data from theHenon Map,
ikedaSim simulates data from the Ikeda Map,
logisticSim simulates data from the Logistic Map,
lorentzSim simulates data from the Lorentz Map,
roesslerSim simulates data from the Roessler Map.

Sorry, currently are implemented only functions to simulate chaotic time maps.

Usage

henonSim(n = 1000, n.skip = 100, parms = c(a = 1.4, b = 0.3), 
        start = runif(2), doplot = FALSE)
ikedaSim(n = 1000, n.skip = 100, parms = c(a = 0.4, b = 6.0, c = 0.9), 
        start = runif(2), doplot = FALSE)
logisticSim(n = 1000, n.skip = 100, parms = c(r = 4), start = runif(1), 
        doplot = FALSE)
lorentzSim(times = seq(0, 40, by = 0.01), parms = c(sigma = 16, r = 45.92, 
        b = 4), start = c(-14, -13, 47), doplot = TRUE, ...)
roesslerSim(times = seq(0, 100, by = 0.01), parms = c(a = 0.2, b = 0.2, c = 8.0),
        start = c(-1.894, -9.920, 0.0250), doplot = TRUE, ...)

Arguments

n, n.skip [henonSim][ikedaSim][logisticSim] -
the number of chaotic time series points to be generated and the number of initial values to be skipped from the series.
parms the parameter vector characterizing the chaotic map.
start the vector of start values to initiate the chaotic map.
doplot a logical value. Should a plot be displayed? By default FALSE.
times [lorentzSim][roesslerSim] -
the sequence of time series points at which to generate the map.
... arguments to be passed.

Value

All functions return invisible a vector of time series data.

Author(s)

Diethelm Wuertz for the Rmetrics R-port.

References

Brock, W.A., Dechert W.D., Sheinkman J.A. (1987); A Test of Independence Based on the Correlation Dimension, SSRI no. 8702, Department of Economics, University of Wisconsin, Madison.

See Also

RandomInnovations.

Examples

## SOURCE("fBasics.A0-SPlusCompatibility")
## SOURCE("fBasics.B4-TestsClass")
## SOURCE("fSeries.A4-TseriesTests")

## bdsTest -
   xmpSeries("\nNext: Brock-Dechert-Sheinkman Test for iid Series >")
   # iid Time Series:
   par(mfrow = c(3, 1))
   x = rnorm(100)
   plot(x, type = "l", main = "iid Time Series")
   bdsTest(x, m = 3)
   # Non Identically Distributed Time Series:
   x = c(rnorm(50), runif(50))
   plot(x, type = "l", main = "Non-iid Time Series")
   bdsTest(x, m = 3)  
   # Non Independent Innovations from Quadratic Map:
   x = rep(0.2, 100)
   for (i in 2:100) x[i] = 4*(1-x[i-1])*x[i-1]
   plot(x, type = "l", main = "Quadratic Map")
   bdsTest(x, m = 3)
   
## tnnTest -
   xmpSeries("\nNext: Teraesvirta NN test for Neglected Nonlinearity >")
   # Time Series Non-linear in "mean" regression 
   par(mfrow = c(2, 1))
   n = 1000
   x = runif(1000, -1, 1)  
   tnnTest(x)
   # Generate time series which is nonlinear in "mean"
   x[1] = 0.0
   for (i in (2:n)) {
     x[i] = 0.4*x[i-1] + tanh(x[i-1]) + rnorm (1, sd = 0.5) }
   plot(x, main = "Teraesvirta Test", type = "l")
   tnnTest(x)
   
## wnnTest -
   xmpSeries("\nNext: White NN test for Neglected Nonlinearity >")
   # Time Series Non-Linear in "mean" Regression
   par(mfrow = c(2, 1))
   n = 1000
   x = runif(1000, -1, 1)
   wnnTest(x)
   # Generate time series which is nonlinear in "mean"
   x[1] = 0.0
   for (i in (2:n)) {
     x[i] = 0.4*x[i-1] + tanh(x[i-1]) + rnorm (1, sd = 0.5) }
   plot(x, main = "White Test", type = "l")
   wnnTest(x)                     

[Package fSeries version 201.10060 Index]