It’s always fascinating to observe outliers and understand them. Outliers are the data points that don’t seem to fit well with rest of the data population. It is interesting that data points with outlier behavior are ‘outlier’ but can be found in almost every dataset that you get your hands on. Identifying outliers is always one of the first few things that a person understanding the data or interpreting the data should do. I would like to argue that it is a sin to infer from data without understanding outliers in that dataset.

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Detection of Outliers in Time Series Data from Control Systems

Outliers are observations which do not fit in the tendency of the time series observed as they differ dramatically from the typical pattern of the trend and/or seasonal components.

Time series data often undergo sudden changes that alter the dynamics of the data. These changes are typically non-systematic and cannot be captured by standard time series models. That’s why they are known as outlier effects. Detecting outliers is important because they have an impact on the selection of the model, the estimation of parameters and consequently, on forecasts. Hence, an approach was followed as described in Chen & Liu (1993) which was published in the Journal of the American Statistical Association, an automatic procedure for detection of outliers in time series i.e  implemented in the package tsoutliers. The function tso is the main interface for the automatic procedure [1].

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