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Short-term Forecast and Analysis of Mass Incidents Based on Time Series Model

机译：基于时间序列模型的群体性事件的短期预测与分析

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摘要

In recent years, the high incidence of mass incidents has seriously threatened the stability and harmonious development of the society. Effective forecasting is very significant to the prevention of and response to mass incidents. Based on the Autoregressive Integrated Moving Average (ARIMA) model and Markov Switching-regime (MS) model, this paper forecasts the number of mass incidents in mainland China in short term. Through the residual error diagnosis of the fitted results and the error analysis of the forecasted results, it is found that the two-regime Markov switching model can achieve a more accurate prediction. The results show that the trend of mass incidents can be divided into two regimes: the stable stage and the peak stage, and the switching between the two stages can be explained by the third-party threats like the economic crisis, political events, and changes in the international situation.
机译：近年来，群体性事件的高发严重威胁着社会的稳定与和谐发展。有效的预测对于预防和应对大规模事件非常重要。基于自回归综合移动平均模型（ARIMA）和马尔可夫切换模型（MS）模型，本文预测了短期内中国大陆的大规模事件数量。通过拟合结果的残差错误诊断和预测结果的误差分析，发现两区域马尔可夫切换模型可以实现更准确的预测。结果表明，群体性事件的趋势可以分为稳定期和高峰期两个阶段，而这两个阶段之间的转换可以用经济危机，政治事件和变化等第三方威胁来解释。在国际形势下。

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