Hi everyone, I have been trying to implement HTM on time series data with a recurring pattern on a weekly basis and am wondering if the anomaly scores I’ve gotten seem reasonable. Here are the plots I got using tm.anomaly to retrieve the anomaly score: https://imgur.com/a/1riezeQ.

In the first graph, since the anomaly scores spiked up at the start of every week, I reduced the number of bits in the SDR representing dayOfWeek/timeOfDay/weekend. Doing that gave me the result shown in the second graph. Why does the anomaly likelihood still spike up every weekend (where there are gaps in the input data)? Is this normal considering I do not reset the TM at any point?

In the 3rd graph in the link, the anomaly score still seems to be spiking at the start of every week even though I am not resetting the TM. Also, the anomaly likelihood has very little spikes even when the prediction and the input are drastically different, and the anomaly likelihood is also gradually increasing and leveling off. I would appreciate any guidance as to whether or not any of these graphs show proper anomaly score/likelihood behavior and if not, what might have gone wrong. Thanks in advance!