outlier detection 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.   
 A: I think that i have helped procedures to do precisely what you want. Given data prior to the most recent value ...."what is the probability that the "new value" is different form what it was supposed to be ?
Can you tell me the probability that a single data point (e.g. the latest
reading) came from the distribution represented by all the previous data points?
As Roger Bacon once said and I have often paraphrased :
To do science is to search for repeated patterns.
To detect anomalies is to identify values that do not follow repeated patterns. For whoever knows the ways of Nature will more easily notice her deviations
and, on the other hand, whoever knows her deviations will more accurately
describe her ways. One learns the rules by observing when the current rules fail.
The whole idea is to automatically develop an auto-projective model and then test whether or not the last observation 
Was consistent with the past. This can be done by developing an ARIMA model and the using 
Intervention Detection to asses the reasonableness of the most recent value.
