Time series binary classification probability smoothing
You can use a total variation regularizer (https://en.wikipedia.org/wiki/Total_variation_denoising), it's a penalty for abrupt changes of neighbor values. It's usually used for images, that's why its TF version (https://www.tensorflow.org/api_docs/python/tf/image/total_variation) operates with 4D tensors, but if you're writing your model in pytorch for instance, it's easy to implement that regularizer yourself. Also possibly you don't need it if you've got enough data and target values there are already smooth. Your ML algorithm would just learn that smoothness from data, the only 2 cases you'd need it is when your dataset is small or when your training targets aren't smooth, but testing targets should be smooth.