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)
