Inability to accumulate minor anomalies?

[rhyolight]

@sqrt42

Due to HTM’s problem with sub-second precision, the data points are always more than one second apart from each other.

It is not a problem, really, just that the system will see those transactions as happening at the same point in time, which should be accurate enough if you are looking for anomalies at a larger time scale.

@sqrt42

However, HTM does not reliably detect 1000 instances of 10 000 being transferred (HTM score is around 0.3).

The “HTM score”… what is that? Are you talking about the “anomaly score”? If so, you should certainly try using the anomaly likelihood process as well. Here is a post where I tried to explain how to use it:

Is my data being predicted correctly?

First off, kudos to both of you for having this conversation while I was away. Thanks @Setus for helping @Addonis out! Try running the anomaly likelihood instead of the raw anomaly score. This is not actually a part of the NuPIC model, but a post-process: As shown in the code above, you create an instance of the anomaly likelihood class and it maintains state. You pass it the value, anomaly score, and timestamp for each point and it will return a more stable anomaly indication than the raw …