Meta-heuristic Methods for Outliers Detection in Multivariate Time Series
Domenico Cucina, Mattheos K. Protopapas, Antonietta di Salvatore · RePEc: Research Papers in Economics · 2008
Abstract In this article we use meta-heuristic meth-ods to detect additive outliers in multivariate time se-ries. The implemented algorithms are: simulated an-nealing, threshold accepting and two dierent versions of genetic algorithm. All of them use the same objec-tive function, the generalized AIC-like criterion, and in contrast with many of the existing methods, they don't require to specify a vector ARMA model for the data and are able to detect any number of potential outliers simultaneously. We used simulated time series and real data to evaluate and compare the performance of the proposed methods.