Rsoner test will detect outliers that are either much smaller or much larger than the rest of the data. Rosner's approach is designed to avoid the problem of masking, where an outlier that is close in value to another outlier can go undetected.Rosner's test is appropriate only when the data, excluding the suspected outliers, are approximately normally distributed, and when the sample size is greater than or equal to 25. My questions is that can we use it on univariate time series or should we apply it for univariate datasets only?
Kabbilawsh Peruvazhuthi
The generalized (extreme Studentized deviation) ESD test (Rosner 1983) is used to identify one or more outliers in a univariate data set with a roughly normal distribution.
Test Statistical Information: Calculate: --> R i = frac, where —>...
Corresponding to the r test statistic, the critical region...
Using the Curve Fit Forecast, Exponential Smoothing Forecast, and Forest-based Forecast tools, you may find outliers at each position of a space-time cube by selecting the Identify outliers option of the Outlier Option parameter.
You need to convert time series to numerical values first, then apply it.
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