I have multiple time series where at any point in the time series, an event can occur that I believe has an effect on the time series. This event can happen at different times for each of the different time series. How would I go about estimating the effect of this event on a time series where the event has not yet occurred? Ideally this would be some function of the time series itself. Thanks!
One approach would be to gauge the difference between the "normal" variations of the time series and the observed quantities at and/or after the event. This approach in three steps (1) MODEL: Build a model for the time series, ideally only using the data before the event, and ideally yet, with a good amount of data so as to make the model robust. (2) INFER: Infer a "prediction" of what the time series "should" look like at/after the event (3) COMPARE: The difference between the prediction and the actual can be taken to be the effect of the event.
You could take standard ARIMA models to for the MODEL part. I would suggest gravitating towards Bayesian models for this purpose.
You can find some more details of (an instance of) this approach here: