  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.
