Description Usage Arguments Value References Examples

This function implements SMVCAPA from Fisch et al. (2019) in an as-if-online way. It detects potentially lagged collective anomalies as well as point anomalies in streaming data.
The runtime scales linearly (up to logarithmic factors) in `ncol(x)`

, `max_lag`

, and `max_seg_len`

. This version of `capa.uv`

has a default value
`transform=tierney`

which uses sequential estimates for transforming the data prior to analysis. It also returns an S4 class which allows the results to be postprocessed
as if the data had been analysed in an online fashion.

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. |

`beta` |
A numeric vector of length p, giving the marginal penalties. If type ="meanvar" or if type = "mean" and maxlag > 0 it defaults to the penalty regime 2' described in Fisch, Eckley and Fearnhead (2019). If type = "mean" and maxlag = 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 the min_seg_len and defaults to Inf. |

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

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

An S4 class of type scapa.mv.class.

2019MVCAPAanomaly

\insertRefalex2020realanomaly

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
library(anomaly)
### generate some multivariate data
set.seed(2018)
x1 = rnorm(500)
x2 = rnorm(500)
x3 = rnorm(500)
x4 = rnorm(500)
### Add two (lagged) collective anomalies
x1[151:200] = x1[151:200]+2
x2[171:200] = x2[171:200]+2
x3[161:190] = x3[161:190]-3
x1[351:390] = x1[371:390]+2
x3[351:400] = x3[351:400]-3
x4[371:400] = x4[371:400]+2
### Add point anomalies
x4[451] = x4[451]*max(1,abs(1/x4[451]))*5
x4[100] = x4[100]*max(1,abs(1/x4[100]))*5
x2[050] = x2[050]*max(1,abs(1/x2[050]))*5
my_x = cbind(x1,x2,x3,x4)
### Now apply MVCAPA
res<-scapa.mv(my_x,max_lag=20,type="mean")
### Examine the output at different times and see how the results are updated:
plot(res,epoch=155)
plot(res,epoch=170)
plot(res,epoch=210)
``` |

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