Detecting outliers in high dimensional time series by dynamic factor models

P. Galeano San Miguel, D. Peña Sánchez de Rivera

A procedure to detect outliers in a large collection of time series is presented.
The high-dimensional setting is handled by assuming that the time series
have been generated by a dynamic factor model and that outliers can appear either in
the latent factors or in the idiosyncratic noise. These two types of outliers can affect
all, many, a few, or just one, of the observed time series and they can be fairly well
detected by projecting the series on the factor and idiosyncratic spaces constructed
from robust estimates of the factor loading matrix. We propose an efficient procedure
based on these linear transformations for detecting outliers. The behavior of the
procedure is illustrated with simulations and the analysis of a real data example.

Keywords: High-dimensional time series; Outliers; Dynamic factor model; Idiosyncratic outliers; Factor outliers

Time Series
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