Recognize and predict a periodic pattern I need to design a controller that tries to collect as much falling objects (raindrops) from an unknown source. Beneficial to solving the problem could be the recognition of the seemingly random generation of the raindrops.

y  ^
   | 0 0 1 0 0 0 0
   | 0 0 1 0 0 1 0
   | 1 0 0 0 0 1 0
   | 0 0 0 1 1 0 0  
   -----------------&gt;
                  t       


So, I have a binary vector signal (raindrop generated yes/no over 4 positions) over discrete time steps. I can only see the raindrops when they are 10 steps away from hitting the ground. The basket has a capacity of one raindrop. The control actions available are step left, step right or do nothing. Any source of pattern recognition could help me design a more advanced controller. Are there methods that can recognize periodic patterns in this type of data?
If it is really close to random (background noise, bits from random number generators or hash codes), I don't expect there is much you can do, except create a large collector. (Or are you only allowed one collector?)

You may want to throw in a few random checks. Those won't prove anything but may give you some insight into the pattern.

If you want to create a successful model, you'd have to come up with some assumptions about parameters that could affect the success of your model, and create and test that model.

For instance, one of my assumptions would be that there are rows with higher probability.
One other assumption would be that there is some hysteresis effect (either positive or negative), so the probability may depend on the actual outcome of the previous event(s).