  We present novel methods for predicting the outcome of large elections. Our
first algorithm uses a diffusion process to model the time uncertainty inherent
in polls taken with substantial calendar time left to the election. Our second
model uses Online Learning along with a novel ex-ante scoring function to
combine different forecasters along with our first model. We evaluate different
density based scoring functions that can be used to better judge the efficacy
of forecasters. We also propose scoring functions which take into account the
entire density of the forecast rather than just a point estimate of the value.
Finally, we consider this framework as a way to improve and judge different
models performing a prediction on the same task.
