# Time series binary classification probability smoothing

Problem

Suppose we have trained binary classifier and want to predict value of [x1, ..., x5] with associated timestamps [t1, ..., t5]. We get the prediction as following: [0.25, 0.99, 0.1, 0.75, 0.79].

Assume that I have the domain knowledge to say that probability of positive class must not change abruptly. Jumps like from 0.99 at t2 to 0.1 at t3 cannot occur in real application.

Questions

1. Can I enforce smooth output constraint on (any/some) classifier?
2. Does applying moving average on the prediction probability to smooth it make sense?

• Ok, I see. Just to clarify, I don't need to do anything with the model itself, the weights will be adjusted by optimization algorithm so as to min(loss + total_variation_penalty), correct? Sep 13 '20 at 17:19