I've got a collection of data that contains observations over time. I want to predict when a given future observation is likely to occur. As a simple example: Say I'm watching billiard balls drop into a pocket. The billiard balls drop in with approximate regularity. My dataset: 1 ball 0:00 2 ball 0:57 3 ball 2:02 4 ball 2:48 5 ball (not observed) 6 ball (not observed) 7 ball 5:55 8 ball (not observed) 9 ball 7:40 ... n ball ? Understand that when the 9 ball drops at 7:40, I know it is the 9 ball. This means I know the 8 ball (and 5 and 6) went in the pocket, even though I didn't observe it and don't know when it did so. These observations are ongoing, so, after each ball, I will update the prediction, getting ever more accurate as I get more data. I want to create an algorithm that will predict when ball n (say, ball 25) is expected to drop. BTW, I am better at programming than math, so I am more comfortable with an algorithm (sequential steps) that a formula.