Hi, Jacob, Thank you for your kind and terrific response.
from what you said,I got the messages:
- The HTM as often the case fails to predict when anomaly occur, because the likelihoods of its predictions are ridiculous then abandoned, instead of the real real data at that time.
Do I get the point? I refer to the source code, there indeed are so called ‘best prediction’ but I couldn’t find the logic of mimicking raw data, and I wonder how the likelihoods of prediction( not the likelihood value of anomaly) are calculated ? Does the process of calculation take advantage of the real data in advance? Still, I have no idea where to get these information in the source code.
- The data model actually outputs right results, it would be better to put more attention on analyzing anomaly score
In my opinion, the curve of anomaly score should keep very high on the appearance of anomalous data, till HTM has leant the pattern. However, this type of case never comes out, what’ more, at some time( An obvious type of Anomalies was not detected by HTM), the curve remains placid amazingly.
- Comparing with abnormal data, the curve of anomaly score should be meek, peaceable and somewhat tractable.
(Ps: the axises (xlim[0,400], ylim[0,28000],in this image are little different from those(xlim[0,800],ylim[-10000,60000] in the first picture. this difference has no effect on the results but may affect the appearances of the curves.)
The image above gives a glimpse of the normal data(about 3500 normal records before the data portrayed in this picture). To be honest, I could not figure out why the curve of anomaly score arises spikes at some places which seem to be normal.