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Human dynamics and sociophysics build on statistical models that can shedlight on and add to our understanding of social phenomena. We propose agenerative model based on a stochastic differential equation that enables us tomodel 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 abrief analysis of the time series of the poll results, we provide empiricalevidence that the gamma distribution, which is often used in financialmodelling, fits the marginal distribution of this time series. We demonstratethat the proposed poll-based forecasting model may improve upon predictionsbased solely on polls. The method uses the Euler-Maruyama method to simulatethe time series, measuring the prediction error with the mean absolute errorand the root mean square error, and as such could be used as part of a toolkitfor forecasting elections.

A stochastic differential equation approach to the analysis of the UK 2017 and 2019 general election polls