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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