Hello Pascal,
Yes of course I mean not predictable by NuPIC not anything else ;) I
should say "Can this be generalized that if NuPIC returns bouncing (non
stable) anomaly score TOO OFTEN...". There is always question what "TOO
OFTEN" means but it is for broader discussion.
No I do not use inference shifter, I was just predicting one step ahead
and when I plot the graph it was obvious what is going on ... I thought
inference shifter is useful with larger prediction steps.
If I understand correct then without inference shifter prediction made
in time T is for data in time T+1. Anomaly score in time T represent the
"confidence of prediction accuracy" in time T if I can say it like that.
Assume one step prediction for above.
If I understand you correct then inference shifter allows you to use
"another column" which puts prediction T+1 to T? I did not catch it well
from your last message. Sorry
Thank you.
On 11/02/2015 12:52 PM, Pascal Weinberger wrote:
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
