NB: I'm the author of the post :) That's a nifty way of doing things that I didn't know as much about (definitely left out the historians bit as I didn't want

NB: I'm the author of the post :) That's a nifty way of doing things that I didn't know as much about (definitely left out the historians bit as I didn't want to get too into the weeds) and have sometimes encountered the linear interpolation fit coming from them as well as the LOCF type fit, but didn't realize that's how they were sampling under the hood. That's pretty cool. I was mostly giving that example so people would understand why the LOCF option was available for the function as well, and one of the most common places where I've seen it is in (usually slowly changing) sensors in industrial settings that only record when they change. On the single continuous process, yes, this is one of the most common mistakes people can make when they do this. We find it's best to model that as a change to the relational part of the data and potentially generate a new "id" for the sensor when that sort of discontinuity happens.