RT Generic
T1 Outlier detection in multivariate time series via projection pursuit
A1 Galeano, Pedro
A1 Peña, Daniel
A1 Tsay, Ruey S.
AB This article uses Projection Pursuit methods to develop a procedure for detecting outliers in a multivariate time series. We show that testing for outliers in some projection directions could be more powerful than testing the multivariate series directly. The optimal directions for detecting outliers are found by numerical optimization of the kurtosis coefficient of the projected series. We propose an iterative procedure to detect and handle multiple outliers based on univariate search in these optimal directions. In contrast with the existing methods, the proposed procedure can identify outliers without pre-specifying a vector ARMA model for the data. The good performance of the proposed method is verified in a Monte Carlo study and in a real data analysis.
YR 2004
FD 2004-09
LK https://hdl.handle.net/10016/215
UL https://hdl.handle.net/10016/215
LA eng
LA eng
DS e-Archivo
RD 19 may. 2024