论文标题
一种随机微分方程方法，用于分析2017年和2019年大选民意调查
A stochastic differential equation approach to the analysis of the UK 2017 and 2019 general election polls
论文作者
论文摘要
人类的动力和社会物理学建立在统计模型的基础上，这些模型可以阐明并增加了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，使我们能够对导致2017年和2019年大选的意见民意调查进行建模，并做出与选举实际结果有关的预测。在对民意调查结果的时间序列进行了简要分析之后，我们提供了经验证据，表明通常用于财务建模的伽马分布适合此时间序列的边际分布。我们证明，拟议的基于民意调查的预测模型可能仅基于民意调查的预测改进。该方法使用Euler-Maruyama方法来模拟时间序列，用平均绝对误差和均方根误差测量预测误差，因此可以用作预测选举的工具包的一部分。
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.
