That’s a great resource. Thanks Subutai. Nick
> On Oct 21, 2014, at 7:34 PM, Subutai Ahmad <[email protected]> wrote: > > > We don't have such a document unfortunately. Scott gave a good presentation > on this at the workshop, including the details. That video should be up some > time in the next few weeks. > > Your second question is a good one. It is something we've struggled with as > well. Some companies have internally developed algorithms, but they don't > release them. You could look at Skyline, which is a pretty decent codebase > for anomaly detection. We have also started an effort to create a good > benchmark for streaming anomaly detection. The current repo is here: > > https://github.com/numenta/NAB <https://github.com/numenta/NAB> > > We discussed this briefly at the workshop. We are actively looking for people > to help us with NAB. In particular we want to collect a lot more data and > finish implementing the scoring mechanisms. If you want to participate that > would be great. > > --Subutai > > On Tue, Oct 21, 2014 at 8:44 AM, Nicholas Mitri <[email protected] > <mailto:[email protected]>> wrote: > Thanks for the tip Subutai! > The wiki page I’m reading doesn’t go into anomaly likelihood. Would you > happen to have a document similar to the one you posted about the CLA > classifier that I can dig more into for the mathematical formulation? > Something that reflects the work done in : > https://github.com/numenta/nupic/blob/b6e5cf3c566e2d6ec60aeae24c4da4db27744138/nupic/algorithms/anomaly.py > > <https://github.com/numenta/nupic/blob/b6e5cf3c566e2d6ec60aeae24c4da4db27744138/nupic/algorithms/anomaly.py> > > I’d also be interested in what algorithms you think would be suitable to test > HTM’s temporal anomaly detection against. I’m unfamiliar in my research with > any alg that’s comparable. > > thanks, > Nick > > >> On Oct 21, 2014, at 6:15 PM, Subutai Ahmad <[email protected] >> <mailto:[email protected]>> wrote: >> >> Hi Nick, >> >> At Numenta we use the difference between predicted and active columns, plus >> the anomaly likelihood calculations. We've had very good results with that >> combination. As mentioned on the wiki page when we tried out confidences >> (over a year ago) we didn't get good results. >> >> --Subutai >> >> On Tue, Oct 21, 2014 at 1:59 AM, Nicholas Mitri <[email protected] >> <mailto:[email protected]>> wrote: >> Thanks Scott. >> For the temporal anomaly detector, the wiki mentions using confidence >> parameters to calculate the anomaly score but the actual code uses >> predictive states instead. Is the latter the final approach Nupic is going >> with? Or should I be looking into reintroducing confidence based anomaly >> scores? >> >> thanks, >> Nick >> >>> On Oct 21, 2014, at 2:21 AM, Scott Purdy <[email protected] >>> <mailto:[email protected]>> wrote: >>> >>> The algorithms are pretty geared around temporal data. If you have purely >>> spatial data like your chart then I wouldn't recommend using NuPIC. You >>> could use the spatial pooler and use the average overlap of active columns >>> with the input bits to approximate it if you really wanted to use NuPIC. >>> >>> On Sun, Oct 19, 2014 at 12:28 PM, Nicholas Mitri <[email protected] >>> <mailto:[email protected]>> wrote: >>> Hey all, >>> >>> I was just reading the anomaly page on the wiki and was curious if there’s >>> an implementation of the non-temporal anomaly detection. >>> I’m running an older build of nupic and I can’t seem to find an anomaly.py >>> file like the one available in the current codebase. >>> >>> I’d like to try it out against other spatial anomaly detectors (euc, >>> manhattan, mahalanobis, etc) and see what kind of boundary it creates in a >>> 2D feature space. >>> The image below is the result of using 1-class SVM as a novelty detector >>> (from scikit-learn tutorials). I’d like to investigate what kind of >>> visualization the spatial pooler and the non-temporal detector would >>> produce. >>> >>> <figure_1.png> >>> >>> best, >>> Nick >>> >> >> > >
