Detecting outliers in multivariate time series using genetic algorithm

摘要

Outlier detection in time series is an important problem and has received a lot of attention in time series analysis. In this paper, we use a genetic algorithm to develop a procedure for detecting different types of outliers in a multivariate time series. This method detects outlier location which maximizes Akaike-like Information Criterion (AIC). The performance of the proposed method is illustrated in a simulation study and a real data analysis.