This depends on the behavior of the item you are measuring. Is this something that moves (or changes) in a manner that can be modeled?
If the item is not expected to change, then your trend should be the underlying mean value of the entire sample set, not just the past two measurements. You can get this using Bayes theorem. The running average can be calculated incrementally using the simple formula
Mtn1 = (Mtn * N + x) / (N+1)
where x is the measurement at time t+1, Mtn1 is the mean a time t+1, Mtn is the mean at time t, and N is the number of measurements taken by time t.
If the item you are measuring fluctuates in a manner that can be predicted by some underlying equation, then you can use a Kalman filter to provide a best estimate of the next point based on the previous (recent) measurements and the equation that models the predicted behavior.
As a starting point, the Wikipedia entry on Bayesian estimators and Kalman Filters will be helpful.