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 2017 and 2019 UK general elections and to make predictions relating to the actual results 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.

人类动力学和社会物理学建立在统计模型的基础上，这些统计模型可以阐明并加深我们对社会现象的理解。我们提出了一个基于随机微分方程的生成模型，该模型使我们能够为2017年和2019年英国大选之前的民意测验建模，并做出与选举实际结果相关的预测。在对民意调查结果的时间序列进行简要分析之后，我们提供了经验证据，即金融建模中经常使用的伽马分布适合该时间序列的边际分布。我们证明了所提出的基于民意测验的预测模型可能会改进仅基于民意测验的预测。该方法使用Euler–Maruyama方法来模拟时间序列，