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A time prediction model for residents consumption level based on ARIMA and PCA

机译：基于Arima和PCA的居民消费水平的时间预测模型

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摘要

It is necessary but difficult to make a large number of observations on multiple variables reflecting the residents consumption level and collect a large amount of data for analysis to search for the rules. In this paper, a time prediction model for residents consumption level based on ARIMA and principal component analysis is proposed to solve this problem. Principal component analysis is firstly used to effectively reduce the number of indicators reflecting the residents consumption level. Combined with the ARIMA model, the residents consumption level is predicted. The results reflect the trend of residents consumption level towards the need for enjoyment and development materials on the basis of obtaining basic survival data.
机译：有必要但难以对反映居民消费水平的多个变量进行大量观察，并收集大量数据进行分析以搜索规则。本文提出了一种基于Arima和主成分分析的居民消费水平的时间预测模型，解决了这个问题。首先用于有效地减少反映居民消费水平的指标数量的主成分分析。结合Arima模型，预测居民消费水平。结果反映了居民消费水平朝着获得基本生存数据的享受和开发材料的需求。

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