QuoteBy "online," I assume you want something that can judge P(outliner) for the most current value of a time series. It also seems you want outlier detection when the distribution is non-Gaussian.Thanks for the tips. Ideally with "online" I also mean doing it in O(1) time and space for each input value, e.g. we might need different volatilities for each covariance so anything more would blow up... This rules out rolling window ranks, but rank against a "fixed" shared datased would work fine... oh well, that's more or less the same as mantaining a cutoff value in the end... but unfortunately we have SV/decay.We also considered 3. which is somewhat dual to tracking a sorted list, but for the tails it's problematic, and again has problems with SV unless your subdivision is fine enough to be impractical... exponential decay would be O(m) (with m thresholds), which then would be expensive. There are also online quantile tracking algos, but they're not immediate to adapt to exponential weighting in an efficient way.(In parallel I'm looking at alternatives avoiding outlier filtering altogether, via heavytail-robust estimators, but we need a quick and dirty solution in the meantime.)QuoteP.S. If the tails of your distribution follow some curve that is linear in some transformed variable space (e.g., the log-log transform for power law tails), then you can create an online curve-fit for the tails.Yep that'd be nice but how does one detect (and mantain) the beginning of the tail? And it all needs to be online...Oh well, if you guys don't have a solution at hand for this I'd also be curious to know how can you live without one
All batch anew each time?