On Mon, Feb 15, 2016 at 4:48 PM, Wakan Tanka <[email protected]> wrote: > On 02/15/2016 05:19 PM, Matthew Taylor wrote: >> >> See below... >> >> On Sat, Feb 13, 2016 at 6:59 PM, Wakan Tanka <[email protected]> wrote: >>> >>> >>> Thank you very much Matt, informative as always ;) >>> >>> One more questions: >>> >>>> 2) Swarming is optimized only for prediction. It may not be the best >>>> method to find model params for anomalies. We have identified a set of >>>> model params that are decent for most one-dimensional scalar input >>>> anomaly detection, and we generally reuse those in all our anomaly >>>> models. >>> >>> > > > So is there better way to find model params for anomaly detection than > swarming?
Watch this: https://www.youtube.com/watch?v=XK5Dd8fGO2w > I know that with using TemporalAnomaly I can predict one step as I were > choose TemporalMultiStep, AFAIK the TemporalMultiStep allows me to predict > multiple steps at once and TemporalAnomaly allows me to predict one step > (with choosen size) plus anomaly score. I'm just curious why people from > Numenta decided to separate them, is it only for peroformance purposes? Also > if I understand correct then predicting larger step will have significant > impact on performance but predicting more will not. I guess that when NuPIC > predicts 10 steps ahead then it under the hood also predicts steps from 1 to > 9. So it is more a matter of memory than CPU. Or am I wrong? We generally do not use the predictions from TemporalAnomaly models. Yes, steps farther in the future will be less performant. Regards, --------- Matt Taylor OS Community Flag-Bearer Numenta
