Hi Karin, Just briefly, I'd like to point you to our repo where we are aiming to verify just that: https://github.com/breznak/ML.benchmark More directly to your question, HTM is an online statistical memory - so the pattern must be repeated several (exactly how many is tricky, depending on "complexity" of the signal) times for HTM to be able to give reasonable predictions/anomaly detection.
Thanks for the good question! Cheers, Mark On Wed, Nov 18, 2015 at 3:13 PM, Karin Valisova <[email protected]> wrote: > Hello guys! > > I am working on a time series analysis thing that has one dimensional data > series as an input and focuses mainly on spotting anomalies. > I'm using nupic, but I want to have a backup plan for situations, where > the data are not appropriate for the network, just to do simple analysis > like detection of the most obvious outliers - ideally before learning the > whole network (which would be easy as I can take a look at various metrics > and draw pretty good conclusions from that). > So I need a set of conditions, based purely on the dataset, to decide if > nupic is usable. The question in fact lies a bit deeper - what are the > necessary attributes of the data, if we want use nupic in general? I can > think size of data sample, should be large enough, how about the degree of > seasonality? > I was thinking about the measurement of seasonality for most common > patterns - like daily and weekly periods and if it's too low then dismiss > the network - but maybe the HTM is able to spot something not obvious? Or > do I expect too much from the algorithm? > > I do realize that the whole concept of performance in the field of > anomaly detection dealing with real time series is a bit hazy, but I would > be really happy to hear your insights and empirical observations on the > matter. > > Thank you! > Karin > -- Marek Otahal :o)
