# Binary classification on time series data

I have a time-series data of air-pressure inside a room. The reading are the output of an physics experiment. The Predictor variable is binary flag which is coded as follows:

If (ending-reading = 0 then 1 else 0)


I have attached the snapshot of the data below. My objective is to predict the likelihood of the ending-reading being 0 for a future time period.

I understand that I can use time-series forecasting like ARIMA or ARIMAX to project the end-reading and then simply refresh the Predictor flag. But I am looking for other alternatives, either supervised or unsupervised methods.

I thought survival-analysis might work but I am not sure if it is applicable in this case since the end-reading can be 0 on multiple days. The experiment doesn't stop if the end-reading on a particular day is 0.

Would logistic regression work on time-series data?

Any help would be much appreciated.