Better than a forgetting factor, add a Kalman filter (http://en.wikipedia.org/wiki/Kalman_filter). This way you can trust your "new" data more than really "old" data, etc. The beauty of it is that it only adds three attributes to each data sample.
The calculation is straightforward once you let some things be the value of identity:
P1 = P0 + Q
K = P0 / (P0 + R)
x1 = x0 + K * (z - x0)
P1 = (1 - K) * P0
x0, P0 - previous score, previous covariance Q - Roughly related to the age of the last measurement. Goes up with age. R - Measurement error. Set it close to 0 if you are sure your measurements are always error-free. z - the most recent measured value.
Let's say you measure number of clicks per 1000 impressions. Now you can estimate the expectation value (x1) for the next 1000. After the second 1000 re-estimate again.
x1 = x0 + alpha * (z - x0)