I have a question regarding outlier detection.
The dataset consists of monthly data for each location. So, for example, the first location "USA" will have values of [8,1,2,1,0] from Jan to May, "Japan" has [2,3,1,20,1], and so on, and I will have to examine whether the future month's data is an outlier when we obtain the data. So, we examine each month.
There seems to be many statistical methods like z-score method, median absolute deviation method, median rule, Tukey's box plot, and so on, which provides a way to detect outliers.
I don't want any machine learning algorithms like one-class SVM because the dataset is too small to train and I don't want to train each time the monthly data comes in.
My question is, there seems to be many detection methods out there including the ones I mentioned above, so how would you go about choosing which method to use?
Also, if you have general recommendations/advice about outlier detection, please let me know as well.