Automating Time Series Forecasting on Crime Data using RNN-LSTM

Criminal activities, be it violent or non-violent are major threats to the safety and security of people. Frequent Crimes are the extreme hindrance to the sustainable development of a nation and thus need to be controlled. Often Police personnel seek the computational solution and tools to realize impending crimes and to perform crime analytics. The developed and developing countries experimenting their tryst with predictive policing in the recent times. With the advent of advanced machine and deep learning algorithms, Time series analysis and building a forecasting model on crime data sets has become feasible. Time series analysis is preferred on this data set as the crime events are recorded with respect to time as significant component. The objective of this paper is to mechanize and automate time series forecasting using a pure DL model. N-Beats Recurrent Neural Networks (RNN) are the proven ensemble models for time series forecasting. Herein, we had foreseen future trends with better accuracy by building a model using NBeats algorithm on Sacremento crime data set. This study applied detailed data pre-processing steps, presented an extensive set of visualizations and involved hyperparameter tuning. The current study has been compared with the other similar works and had been proved as a better forecasting model. This study varied from the other research studies in the data visualization with the enhanced accuracy. © 2021