基于ARIMA(自回归综合移动平均)模型的时间序列分析

Neetu Faujdar, Anant Joshi
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引用次数: 0

摘要

随着数据分析和数据挖掘领域的巨大进步,数据可视化获得了新的重要性。数据可视化侧重于对复杂的数据进行可视化和抽象,使其易于理解和理解,使用信息的可视化表示。在过去的十年里,犯罪分析和与犯罪有关的数据一直在稳步普及,本章旨在将这些数据可视化。在本章中,世界上许多国家的几种不同类型的犯罪数据被收集、编译、处理、分析和可视化。使用时间序列分析对这些数据进行了预测分析。本章旨在创建一个中心,互联网用户可以很容易地查看和解释这些数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time Series Analysis for Crime Forecasting Using ARIMA (Autoregressive Integrated Moving Average) Model
With massive advancements in the fields of data analysis and data mining, a new importance has been gained by data visualization. Data visualization focuses on visualizing and abstracting complex data to make it comprehensible and easy to understand using visual representation of information. Analysis of crime and crime-related data has been steadily popularizing over the last decade, and this chapter aims at visualizing such data. Crime data for several different types of crime for many countries in the world has been collected, compiled, processed, analyzed, and visualized in this chapter. Predictive analysis of this data has also been performed using time series analysis. This chapter aims to create a hub where internet users can easily view and interpret this data.
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