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)
