  Human dynamics and sociophysics build on statistical models that can shed
light on and add to our understanding of social phenomena. We propose a
generative model based on a stochastic differential equation that enables us to
model the opinion polls leading up to the UK 2017 and 2019 general elections,
and to make predictions relating to the actual result of the elections. After a
brief analysis of the time series of the poll results, we provide empirical
evidence that the gamma distribution, which is often used in financial
modelling, fits the marginal distribution of this time series. We demonstrate
that the proposed poll-based forecasting model may improve upon predictions
based solely on polls. The method uses the Euler-Maruyama method to simulate
the time series, measuring the prediction error with the mean absolute error
and the root mean square error, and as such could be used as part of a toolkit
for forecasting elections.
