| ChaoticTimeSeries {fSeries} | R Documentation |
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.
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, ...)
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. |
All functions return invisible a vector of time series data.
Diethelm Wuertz for the Rmetrics R-port.
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.
RandomInnovations.
## 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)