The data I used just the
nyc_taxi.csv with some artificial anomalies. the normal data patterns are just similar to the pattern in the green cycle. And the artificial anomalies are just in the red cycle. This time, What puzzles me most is the HTM fail to give reasonable predictions, instead, just following the raw data’s steps.
From where I stand, this is a seriously anomaly for HTM, as the ground truth that HTM has the ability of anomaly detection is just based on its prediction ability. Therefore, the loss of ability of prediction no doubt leads to fail to detect anomaly (in red cycle, especially in orange cycle.)
as for the program I used just modify the code for
one_gym example, in which I change the range of data, and the
timeofDay radius( to 0.5).
To be honest, I love HTM, as its base thoughts are beautiful and elegant. However, I couldn’t apply it to my project if its ability loses at such an extreme and obvious anomaly situation. In my heart, I have great confidence in HTM’s theory, so please help me to find out the reason of such an abnormal failure.