Tom, Good Question,
An anomaly is something that is not expected. Anomalies lie at the edge of known behavior\data and unknown\unseen behavior\data. Once an anomaly is seen it does not stay an anomaly any more. So Anomalies by themselves are not a tool to use, although thats how Nupic may have been thought of in early days. What you need is association of behavior with labeled patterns, so when such a behavior\pattern occurs you know what it is, and its assotiation with labels that are positive or negative or associated with other events . When you see the downward spikes, it may not result in an anomaly , but results in an inference of known states. I am working on something around this area. Chandan On Wed, Jun 24, 2015 at 6:49 PM, Marek Otahal <[email protected]> wrote: > I think this one would help - supervised metric for anomaly detection: > https://github.com/numenta/nupic/issues/1830 > > I might get to work on it soon, hopefully > > On Thu, Jun 25, 2015 at 2:08 AM, Marek Otahal <[email protected]> > wrote: > >> Tom, >> >> I've seen similar when working with ECG signal. >> >> 1/ I think your HTM is too quick about picking up changes. I think it >> learned to model/repeat just the last "beat" - that is actually a pretty >> good strategy and works most of the time! >> To unlearn this you can try: >> -reducing #columns (thus giving the network less computational resources, >> so it has to abstract more) >> -modify params that effect learning speed (permanence inc/dec, >> #cells/col, look back steps, ..what else??) >> -change metric so it has a big penalty for the mistake and drives HTM to >> unlearn the 1-beat pattern.. >> >> 2/ there's some information occurring before the drop and HTM exploited >> it and is able to detect the "anomaly" faster then you! :) >> >> >> On Thu, Jun 25, 2015 at 1:29 AM, Tom Tan <[email protected]> wrote: >> >>> >>> Hi, >>> >>> I tried to use Nupic for anomaly detection over following data set. The >>> blue line is actual and red line is Nupic prediction. The downward spikes, >>> such as the one circled out, are anomalies in our case. >>> >>> Nupic seems to treat the anomaly as regular pattern and later predicts >>> such downward spikes. It can be shown that the red spikes later follow the >>> blue spike. However, downwards spikes are true anomalies and should not >>> be accounted as norm. Is there a way to suppress such predictions? >>> >>> Regards, >>> Tom >>> >>> >>> >>> >>> >> >> >> -- >> Marek Otahal :o) >> > > > > -- > Marek Otahal :o) > -- Regards Chandan Maruthi
