Description Usage Arguments Value References Examples

A technique for detecting anomalous segments and points based on CAPA (Collective And Point Anomalies) by Fisch et al. (2018). This is a generic method that can be used for both univariate
and multivariate data. The specific method that is used for the analysis is deduced by `capa`

from the dimensions of the data.

1 2 3 4 5 6 7 8 9 10 |

`x` |
A numeric matrix with n rows and p columns containing the data which is to be inspected. The time series data classes ts, xts, and zoo are also supported. |

`beta` |
A numeric vector of length p, giving the marginal penalties. If p > 1, type ="meanvar" or type = "mean" and max_lag > 0 it defaults to the penalty regime 2' described in Fisch, Eckley and Fearnhead (2019). If p > 1, type = "mean"/"meanvar" and max_lag = 0 it defaults to the pointwise minimum of the penalty regimes 1, 2, and 3 in Fisch, Eckley and Fearnhead (2019). |

`beta_tilde` |
A numeric constant indicating the penalty for adding an additional point anomaly. It defaults to 3log(np), where n and p are the data dimensions. |

`type` |
A string indicating which type of deviations from the baseline are considered. Can be "meanvar" for collective anomalies characterised by joint changes in mean and variance (the default), "mean" for collective anomalies characterised by changes in mean only, or "robustmean" for collective anomalies characterised by changes in mean only which can be polluted by outliers. |

`min_seg_len` |
An integer indicating the minimum length of epidemic changes. It must be at least 2 and defaults to 10. |

`max_seg_len` |
An integer indicating the maximum length of epidemic changes. It must be at least min_seg_len and defaults to Inf. |

`max_lag` |
A non-negative integer indicating the maximum start or end lag. Only useful for multivariate data. Default value is 0. |

`transform` |
A function used to centre the data prior to analysis by |

An instance of an S4 class of type capa.class.

2018arXiv180601947Fanomaly

1 2 3 4 5 6 7 8 | ```
library(anomaly)
# generate some multivariate data
set.seed(0)
sim.data<-simulate(n=500,p=100,mu=2,locations=c(100,200,300),
duration=6,proportions=c(0.04,0.06,0.08))
res<-capa(sim.data,type="mean",min_seg_len=2,max_lag=5)
collective_anomalies(res)
plot(res)
``` |

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