Not predictable using nupic, but as Matt said, give it it's time, or better data until you interpret or judge anything :)
But one quick question regarding you first point, if you use the inference shifter (as it is done in most tutorials) then the results of the model are already shifted to where they belong. So nupic outputs prediction and anomaly to (in your case) t+1 but the inference shifter puts them there as well... So do you use it? Best, Pascal Weinberger ____________________________ BE THE CHANGE YOU WANT TO SEE IN THE WORLD ... > On 02 Nov 2015, at 10:57, Wakan Tanka <[email protected]> wrote: > >> On 11/02/2015 06:12 AM, Matthew Taylor wrote: >>> On Sun, Nov 1, 2015 at 2:26 PM, Wakan Tanka <[email protected]> wrote: >>> 1. If this is one step ahead prediction then the prediction value on >>> line n should correspond to the original value on line n+1 >>> (assuming that NuPIC made good prediction and not mistake)? >> >> If the prediction is perfectly right, yes. >> >>> 2. If first question is true can you please explain me the 179 line? On >>> line 179 there is prediction which equals 0 and on line 180 original >>> value equals to 0 which is OK. But why I get anomaly score 1 on line >>> 179? >> >> Just because the best prediction is correct does not mean that the HTM >> is confident that it is correct. For example, NuPIC might only be 23% >> confident in the best prediction it gives, in which case the anomaly >> score could be very high. >> >>> 3. Or you can look at it vice versa: Prediction on line 180 is equal to >>> 0 but the original value on line 181 is 3. So I assume prediction >>> was wrong. Why anomaly score on line 180 equals to 0? Does it means >>> that NuPIC believe that it is predicting the correct value but in >>> fact it was wrong? >> >> I would not pay too much attention to the anomaly score (or >> predictions for that matter) until the model has seen a few thousand >> rows of data. It looks like it has seen less than 200 rows as this >> point, so the anomaly scores can vary wildly until it establishes what >> the data patterns are. >> >> Regards, >> --------- >> Matt Taylor >> OS Community Flag-Bearer >> Numenta > > Hello Matt, > > Can this be generalized that if NuPIC returns bouncing (non stable) anomaly > score then it is either because NuPIC does not see enough data or because the > data are not predictable? > > Thank you very much >
