Data mining is the process in which we extract the different patterns and useful Information from large dataset. According to London police, crimes are immediately increases from beginning of 2017 in different borough of London. No useful information is available for prevent crime on future basis. We forecasts crime rates in London borough by extracting large dataset of crime in London and predicted number of crimes in future. We used time series ARIMA model for forecasting crimes in London. By giving 5 years of data to ARIMA model forecasting 2 years crime data. Comparatively, with exponential smoothing ARIMA model has higher fitting values. A real dataset of crimes reported by London police collected from its website and other resources. Our main concept is divided into four parts. Data extraction (DE), data processing (DP) of unstructured data, visualizing model in IBM SPSS. DE extracts crime data from web sources during 2012 for the 2016 year. DP integrates and reduces data and give them predefined attributes. Crime prediction is analyzed by applying some calculation, calculated their moving average, difference, and auto-regression. Forecasted Model gives 80% correct values, which is formed to be an accurate model. This work helps for London police in decision-making against crime.

中文翻译：

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使用 ARIMA 模型预测犯罪

数据挖掘是我们从大型数据集中提取不同模式和有用信息的过程。据伦敦警方称，从 2017 年初开始，伦敦不同区的犯罪率立即增加。没有有用的信息可用于在未来预防犯罪。我们通过提取伦敦犯罪的大量数据集来预测伦敦自治市的犯罪率，并预测未来的犯罪数量。我们使用时间序列 ARIMA 模型来预测伦敦的犯罪。通过向 ARIMA 模型提供 5 年的数据来预测 2 年的犯罪数据。相比之下，指数平滑的 ARIMA 模型具有更高的拟合值。伦敦警方从其网站和其他资源收集的真实犯罪数据集。我们的主要概念分为四个部分。非结构化数据的数据提取（DE）、数据处理（DP）、IBM SPSS 中的可视化模型。DE 从网络资源中提取 2012 年 2016 年的犯罪数据。DP 集成和减少数据并赋予它们预定义的属性。通过应用一些计算来分析犯罪预测，计算它们的移动平均值、差异和自回归。预测模型给出了 80% 的正确值，这是一个准确的模型。这项工作有助于伦敦警方制定打击犯罪的决策。