An Investigation into Crime Forecast Using Auto ARIMA and Stacked LSTM

The severity of criminal activities which cause both physical and psychological damage has been increasing at an alarming rate across the globe. Realizing the significance of this problem, law enforcement agencies have developed several strategies to prevent crimes. Being slow-paced and ineffective in most cases, these prevention strategies are not robust enough to contribute in predicting crime trends for an early prevention. In this paper, we propose a regression-based model that incorporates temporal, statistical relationships and other relevant information about the data to forecast crime trends. Since, seasonal information is a powerful inclusion in an application of time series pattern, we use two popular regression methods, including an extended Autoregressive Integrated Moving Average (Auto ARIMA) and stacked Long Short-Term Memory (LSTM) to analyze crime patterns, specifically during the Covid-19 pandemic lockdown, and generate forecasts. We experimented our methods on London Crime Dataset and obtained some interesting results which can not only be useful to take necessary precautions, but also analyze crime patterns during the period of pandemic lockdowns for generating useful guidelines regarding citizens’ life styles and hence, contribute to reducing the crime rates accordingly.