time series forecast, dependent variable is binary

I have time series data, where the dependent variable is binary - either 0 or 1. The 0 value means failure, it's rare, and I want to see if I can get close to estimating the times when it will happen in the future.

A naive, completely unoptimized application of Facebook Prophet yielded this (the handful of 0 values are at the bottom of the graph; most values are 1):

Before I continue tweaking the model, are there any reasons why I should not continue to work on it? I know Prophet is made for continuous variables, not categorical or even binary. But I was hoping I could use the output of the predictive model as a proxy for the likelihood that the failure may occur at some point in the future.

I also plan to write an LSTM model in PyTorch and try it.

Anything else I should look at?