# Confusion matrix for events in a time series

I'm not big on statistics, so please excuse my ignorance.

I have a video recording that I want to evaluate, I have an algorithm that can transform this video into a time series where I have 0 everywhere except for a couple of frames where an event (A) occurs.

Then I have manual annotations of another event (B) that I think is related to the 1st event (A) (i.e. event B appears shortly after event A).

I want to construct a confusion matrix like this:

1. Event A is condition, event B is "test"
2. True positive value is when there is an event A and within 50 frames there is also event B
3. False positive is when A is not present, but B is
4. False negative is when A is present, but B is not

Now my problem is about True Negatives. If you do vaccine testing, you have a total number of tests, and you can quantify True Negatives without issue. But what about my case? I either have event A or B but True Negative is by definition everything else in the time series?

Does it make sense to even use confusion matrix?

• Reading it more carefully, if you're using A as an input signal to predict event B, then your error types seem to be swapped - false positive would be when you predicted B (had seen A) but B didn't happen; and false negative would be when you didn't predict B (didn't see A), but B was actually present. So #3 and #4 would be opposite to what you have written now. May 20 '14 at 7:11