Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equivalents have been embraced before. In this work, we use a strategy for a time series model and machine testing systems for crime estimation. The paper centers on determining the quantity of crimes. Considering various experimental analyses, this investigation additionally features results obtained from a neural system that could be a significant alternative to machine learning and ordinary stochastic techniques. In this paper, we applied various techniques to forecast the number of possible crimes in the next 5 years. First, we used the existing machine learning techniques to predict the number of crimes. Second, we proposed two approaches, a modified autoregressive integrated moving average model and a modified artificial neural network model. The prime objective of this work is to compare the applicability of a univariate time series model against that of a variate time series model for crime forecasting. More than two million datasets are trained and tested. After rigorous experimental results and analysis are generated, the paper concludes that using a variate time series model yields better forecasting results than the predicted values from existing techniques. These results show that the proposed method outperforms existing methods.

犯罪预测一直是当今执法中最复杂的挑战之一，特别是当分析倾向于评估可推断和扩大的犯罪率时，尽管之前采用了一些等效的方法。在这项工作中，我们使用时间序列模型策略和用于犯罪估计的机器测试系统。本文的重点是确定犯罪数量。考虑到各种实验分析，该研究还额外介绍了从神经系统获得的结果，该结果可能是机器学习和普通随机技术的重要替代方法。在本文中，我们应用了各种技术来预测未来5年内可能发生的犯罪数量。首先，我们使用现有的机器学习技术来预测犯罪数量。第二，我们提出了两种方法，一种改进的自回归综合移动平均模型和一种改进的人工神经网络模型。这项工作的主要目的是比较单变量时间序列模型和变量时间序列模型在犯罪预测中的适用性。超过200万个数据集经过培训和测试。经过严格的实验结果和分析，得出结论，使用可变时间序列模型比现有技术的预测值能产生更好的预测结果。这些结果表明，所提出的方法优于现有方法。这项工作的主要目的是比较单变量时间序列模型和变量时间序列模型在犯罪预测中的适用性。超过200万个数据集经过培训和测试。经过严格的实验结果和分析，得出结论，使用可变时间序列模型比现有技术的预测值能产生更好的预测结果。这些结果表明，所提出的方法优于现有方法。这项工作的主要目的是比较单变量时间序列模型和变量时间序列模型在犯罪预测中的适用性。超过200万个数据集经过培训和测试。经过严格的实验结果和分析后，本文得出结论，使用可变时间序列模型比现有技术的预测值产生更好的预测结果。这些结果表明，所提出的方法优于现有方法。