Hi Omair, Thanks! We have only done rough comparisons so far with standard models. One problem has been the lack of good labeled datasets for streaming anomaly detection (which is the specific application we've tuned for). We have been accumulating some real world data and hope to put together a dataset for more rigorous testing. I hope to release that soon. If you know of some good datasets focused on streaming data, that would be really helpful - we could add them to the mix.
In the meantime I can make some high level observations: 1) HTM's are not more computationally efficient. There's a lot more going on. It is reasonably fast - it takes about 25 msecs on my laptop to process one data point. MA models are probably an order of magnitude faster though. 2) HTM's can learn many different patterns, not just one pattern. It can also make multiple predictions at each time step. With the likelihood code it can also learn high level statistics about the data and thus can work with highly unpredictable data. 3) Anecdotally, with Grok, we've seen many examples of HTM anomaly detection that probably would not have been caught by MA models. 4) I did do a comparison of HTM prediction against ARIMA (I believe ARIMA is a superset of Holt-Winters). HTM did a lot better on one energy dataset. There was a discussion of it here: http://lists.numenta.org/pipermail/nupic_lists.numenta.org/2013-September/001411.html Hope this helps! --Subutai On Fri, Jul 25, 2014 at 10:22 AM, Omair Shafi <[email protected]> wrote: > Hi again, > > I've been fooling around the Anomaly detection sample code by Subutai > using TemporalAnomaly, which seems absolutely awesome! I just wanted to > know how it compared with standard models for anomaly detection like Holt > Winters, other variations of Moving Average and the lot. Does it score > higher on accuracy? Or is it more computationally efficient? Or maybe it > can learn more than one pattern? > > Thanks! > > Regards, > Omair S > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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