  Forecasting elections -- a challenging, high-stakes problem -- is the subject
of much uncertainty, subjectivity, and media scrutiny. To shed light on this
process, we develop a method for forecasting elections from the perspective of
dynamical systems. Our model borrows ideas from epidemiology, and we use
polling data from United States elections to determine its parameters.
Surprisingly, our general model performs as well as popular forecasters for the
2012 and 2016 U.S. races for president, senators, and governors. Although
contagion and voting dynamics differ, our work suggests a valuable approach to
elucidate how elections are related across states. It also illustrates the
effect of accounting for uncertainty in different ways, provides an example of
data-driven forecasting using dynamical systems, and suggests avenues for
future research on political elections. We conclude with our forecasts for the
senatorial and gubernatorial races on 6~November 2018, which we posted on 5
November 2018.
