We have found that the likelihood value is generally more useful as a metric. Your work will now be to decide the threshold for this value, and 0.95 is probably a good place to start. Move this up and down to get to a place where truly anomalous behavior is flagged, while other behavior is not.
For the anomaly likelihood, we actually set thresholds like 0.99995 (yellow) or 0.99999 (red). There is also the “log likelihood” that converts from that range to a more visualizable range of values that we set thresholds of 0.4 (yellow) or 0.5 (red).
In other words, I’d expect a LOT of false positives with a threshold of 0.95 on the anomaly likelihood.
We don’t have direct comparisons of HTM to TensorFlow, but you must understand that TensorFlow is a platform for machine learning, and you can run many algorithms with it. It is set up for spatial scaling and processing, not necessarily temporal. There is no comparison to make here, really.
However, we do have benchmarks comparing HTM doing streaming scalar anomaly detection vs other ML techniques that do handle temporal data (LSTM for example) in the Numenta Anomaly Benchmark.