# Accurate Prediction of Electoral Outcomes

## TL;DR

This paper introduces new probabilistic models and scoring methods for predicting electoral outcomes, aiming to improve forecast accuracy and evaluation by leveraging diffusion processes and online learning techniques.

## Contribution

It presents novel diffusion and online learning models for election prediction, along with density-based scoring functions for better forecast evaluation.

## Key findings

- Diffusion model effectively captures poll uncertainty over time.
- Online learning combined with new scoring functions improves forecast accuracy.
- Density-based scoring functions provide a comprehensive assessment of forecast quality.

## Abstract

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.