Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem. (1st April 2021)

• Record Type: Journal Article
• Authors: Tariq, Haseeb; Hanif, Muhammad Kashif; Sarwar, Muhammad Umer; Bari, Sabeen; Sarfraz, Muhammad Shahzad; Oskouei, Rozita Jamili
• Other Names: Nazir Shah Academic Editor.
• Is Part Of: Security and communication networks. Volume 2021(2021)
• Publication Date: 2021-04-01
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2021/5587511
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Crime is a bone of contention that can create a societal disturbance. Crime forecasting using time series is an efficient statistical tool for predicting rates of crime in many countries around the world. Crime data can be useful to determine the efficacy of crime prevention steps and the safety of cities and societies. However, it is a difficult task to predict the crime accurately because the number of crimes is increasing day by day. The objective of this study is to apply time series to predict the crime rate to facilitate practical crime prevention solutions. Machine learning can play an important role to better understand and analyze the future trend of violations. Different time-series forecasting models have been used to predict the crime. These forecasting models are trained to predict future violent crimes. The proposed approach outperforms other forecasting techniques for daily and monthly forecast.