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
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## 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)