Comparison of Different Methods of Outlier Detection in Univariate Time Series Data
DOI:
https://doi.org/10.53555/ms.v1i1.912Keywords:
Univariate time series data, outlier detection, MADe Rule,, Modified Z-Score, 2SD method, 3SD methodAbstract
Overtime, different methods of detecting outliers have been worked on, some detected single outliers while others detected multiple outliers, some detected outliers in univariate models while others are limited to multivariate models, some others used simple measures while a lot others used the robust measures for detecting outliers. With these numerous methods raised the problem of which method is the best given a particular set of data. The best methods are subjective to the kind of data that is under consideration in the given study. For this study, we confined our attention to univariate time series data, subjected it to different methods of outlier detection in univariate data, detected the outliers and then worked on the efficiency of these different methods of outlier detection. We as well took time to outline the procedures of detecting univariate outlier in some common statistical software packages. It can be concluded from the evidence of this study that the 3SD method and the Z-score method of outlier detection is not a good model for detecting outliers in univariate model. This can be attributed to the parameters they use for estimation of outliers in these data sets.
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