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Detecting outliers in multivariate time series using genetic algorithm

Ahad Rahimpoor, Masoud Yarmohammadi


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.


genetic algorithm, multivariate time series, outliers, AIC.

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