Description Usage Arguments Value References Examples

View source: R/outliers.hdts.R

Outlier detection in high dimensional time series by using projections as in Galeano, Peña and Tsay (2006).

1 | ```
outliers.hdts(x, r.max, type)
``` |

`x` |
T by k data matrix: T data points in rows with each row being data at a given time point, and k time series in columns. |

`r.max` |
The maximum number of factors including stationary and non-stationary. |

`type` |
The type of series, i.e., 1 if stationary or 2 if nonstationary. |

A list containing:

x.clean - The time series cleaned at the end of the procedure (n x m).

P.clean - The estimate of the loading matrix if the number of factors is positive.

Ft.clean - The estimated dynamic factors if the number of factors is positive.

Nt.clean - The idiosyncratic residuals if the number of factors is positive.

times.idi.out - The times of the idiosyncratic outliers.

comps.idi.out - The components of the noise affected by the idiosyncratic outliers.

sizes.idi.out - The sizes of the idiosyncratic outliers.

stats.idi.out - The statistics of the idiosyncratic outliers.

times.fac.out - The times of the factor outliers.

comps.fac.out - The dynamic factors affected by the factor outliers.

sizes.fac.out - The sizes of the factor outliers.

stats.fac.out - The statistics of the factor outliers.

x.kurt - The time series cleaned in the kurtosis sub-step (n x m).

times.kurt - The outliers detected in the kurtosis sub-step.

pro.kurt - The projection number of the detected outliers in the kurtosis sub-step.

n.pro.kurt - The number of projections leading to outliers in the kurtosis sub-step.

x.rand - The time series cleaned in the random projections sub-step (n x m).

times.rand - The outliers detected in the random projections sub-step.

x.uni - The time series cleaned after the univariate substep (n x m).

times.uni - The vector of outliers detected with the univariate substep.

comps.uni - The components affected by the outliers detected with the univariate substep.

r.rob - The number of factors estimated (1 x 1).

P.rob - The estimate of the loading matrix (m x r.rob).

V.rob - The estimate of the orthonormal complement to P (m x (m - r.rob)).

I.cov.rob - The matrix (V'GnV)^-1 used to compute the statistics to detect the idiosyncratic outliers.

IC.1 - The values of the information criterion of Bai and Ng.

Galeano, P., Peña, D., and Tsay, R. S. (2006). Outlier detection in
multivariate time series by projection pursuit. *Journal of the American Statistical Association*, 101(474), 654-669.

1 2 | ```
data(TaiwanAirBox032017)
output <- outliers.hdts(as.matrix(TaiwanAirBox032017[1:100,1:3]), r.max = 1, type =2)
``` |

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