I am using HTM to implement data anomaly detection
According to my understanding:
HTM continuously learns the data pattern and predicts the next input, and gets anomalous scores by measuring the deviation of the model’s predicted input from the actual input.
and I think:
When the data is abnormal, the network will still update the synaptic connection.
The idea comes from:
The given data is a sequence anomaly, but the HTM test results show that some points in this sequence are abnormal and the other points are normal. I guess the above situation is caused.
If so, whether the point considered abnormal can be fed back to the HTM model to restore the synaptic connection to the value of the previous step.
Any help will be less appreciated.