Hi Mark, The repo looks nice! It will be really interesting to see what benchmark data you come up with!
--Subutai On Wed, Nov 18, 2015 at 7:13 AM, Marek Otahal <[email protected]> wrote: > 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) >
