论文标题
使用Arima模型预测犯罪
Forecasting Crime Using ARIMA Model
论文作者
论文摘要
数据挖掘是我们从大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，犯罪立即增加。没有有用的信息可预防将来的犯罪。我们通过在伦敦提取大量犯罪数据集并预测将来的犯罪数量来预测伦敦自治市镇的犯罪率。我们使用时间序列Arima模型在伦敦进行预测犯罪。通过向Arima模型提供5年的数据预测2年犯罪数据。相比之下，使用指数平滑的Arima模型具有较高的拟合值。伦敦警察报告的真正犯罪数据集是从其网站和其他资源中收集的。我们的主要概念分为四个部分。数据提取（DE），非结构化数据的数据处理（DP），可视化IBM SPS中的模型。 DE在2016年的2016年中从网络来源提取犯罪数据。 DP集成并减少数据并为它们提供预定义的属性。通过应用一些计算，计算其移动平均值，差异和自动回归来分析犯罪预测。预测模型给出80％的正确值，形成为准确的模型。这项工作有助于伦敦警察对犯罪的决策。
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.
