Thanks Subutai! You mentioned the project to me in a pervious exchange but I couldn’t find it. I’ll have a look at the code. HTM didn’t perform too well in a set of classification experiments I prepared for my thesis so I really wanted to showcase it doing what it does best. Unfortunately, I only had the hot gym example which I don’t think offers enough insight into how HTM can be put to the best use. Hopefully, the NAB project can offer a better alternative.
Thanks again, Nick > On Mar 1, 2015, at 7:18 PM, Subutai Ahmad <[email protected]> wrote: > > Hi Nick, > > Are you referring to NAB (Numenta Anomaly Benchmark)? The code for it is > here: > > https://github.com/numenta/NAB <https://github.com/numenta/NAB> > > The purpose of NAB is to establish a benchmark for real time anomaly > detection. One of the goals is to include actual real-world sensor data with > labeled anomalies. We’re at an “alpha” stage right now so it is not fully > complete but you can look through it (there’s a doc on the wiki). > > NAB is not specific to Numenta. We’ve included Skyline (a popular open source > anomaly detection algorithm) but we hope over time people will add other > algorithms. > > It’s not fully ready yet but I’m happy to go over details in the next office > hour if there is interest. We could really use help from anyone who can > provide real sensor/machine data with anomalies. > > —Subutai > > > > On Fri, Feb 27, 2015 at 3:31 PM, Nicholas Mitri <[email protected] > <mailto:[email protected]>> wrote: > Hey all, > > There was talk of an ongoing project to benchmark HTM against a set of > algorithms. > Any updates on that? > > I’d be interested to see what algorithms the Numenta team finds comparable to > HTM. > > best, > Nick >
