Description Usage Arguments Details Value References Examples

Generate piecewise stationary time series with independent innovations and change points in the mean.

1 2 3 4 5 6 7 8 9 |

`model` |
a string indicating from which model a realisation is to be generated;
possible values are "custom" (for user-specified model
using |

`lengths` |
use iff |

`means` |
use iff |

`sds` |
use iff |

`rand.gen` |
optional; a function to generate the noise/innovations |

`seed` |
optional; if a seed value is provided ( |

`...` |
further arguments to be parsed to |

See Appendix B in the reference for details about the test signals.

a list containing the following entries:

x a numeric vector containing a realisation of the piecewise time series model, given as signal + noise

mu mean vector of piecewise stationary time series model

sigma scaling vector of piecewise stationary time series model

cpts a vector of change points in the piecewise stationary time series model

P. Fryzlewicz (2014)
Wild Binary Segmentation for Multiple Change-Point Detection.
*The Annals of Statistics*, Volume 42, Number 6, pp. 2243-2281.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# visualise estimated changepoints by solid vertical lines
# and true changepoints by broken vertical lines
td <- testData(lengths = c(50, 50, 200, 300, 300), means = c(0, 1, 2, 3, 2.3),
sds = rep(1, 5), seed = 123)
mbu <- multiscale.bottomUp(td$x)
plot(mbu, display = "data")
abline(v = td$cpts, col = 2, lwd = 2, lty = 2)
# visualise estimated piecewise constant signal by solid line
# and true signal by broken line
td <- testData("blocks", seed = 123)
mlp <- multiscale.localPrune(td$x)
plot(mlp, display = "data")
lines(td$mu, col = 2, lwd = 2, lty = 2)
``` |

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