California experienced an increase in violent criminality during the last decade, largely driven by a surge in aggravated assaults. To address this challenge, accurate and timely forecasts of criminal activity may help state authorities plan ahead and distribute public resources efficiently to reduce crime. This paper forecasts monthly aggravated assaults in California using a publicly available dataset on state crimes and a time series SARIMA model that incorporates the highly seasonal behavior observed in the data. Results show that predictions with reasonable accuracy up to six months in advance are produced, showing the usefulness of these techniques to anticipate state-level criminal patterns and inform public policy.

翻译：在过去十年中，加利福尼亚州的暴力犯罪有所增加，这主要由严重袭击事件的激增所驱动。为应对这一挑战，准确且及时的犯罪活动预测可帮助州政府当局提前规划并高效分配公共资源以降低犯罪率。本文利用公开的州级犯罪数据集，结合体现数据中高度季节性行为的时间序列SARIMA模型，对加利福尼亚州的每月严重袭击事件进行预测。结果表明，该方法能够产生长达六个月的合理精度预测，展现了这些技术在预测州级犯罪模式及为公共政策提供信息方面的实用性。