本文对消费者物价指数上涨条件下城市居民食品零售价格预测问题进行了研究。针对样本数据离散、随时间变化、相互关联、受季节影响、周期性或不规则变动等特点建立了在经济学中应用十分广泛的时间序列分析模型对食品零售价格预测,综合运用数理统计、归一化处理、聚类分析、时间序列等方法,结合多种模型对所提出问题进行了研究,具有很好的实用性与推广性。 This paper studies the prediction of urban residents’ retail price of food under the condition of rising consumer price index. Aiming at the discrete, time-varying, interdependent, seasonal, or irregular changes of sample data, a series of time series analysis models widely used in economics are used to predict the retail price of food. The comprehensive use of mathematical statistics A combination of processing, clustering analysis, time series and other methods, combined with a variety of models on the proposed issues have been studied, has good practicality and promotion.