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Based on the online transaction data of COSCO group's centralized procurementplatform, this paper studies the clustering method of time series type data.The different methods of similarity calculation, different clustering methodswith different K values are analysed, and the best clustering method suitablefor centralized purchasing data is determined. The company list under thecorresponding cluster is obtained. The time series motif discovery algorithm isused to model the centroid of each cluster. Through ARIMA method, we also made12 periods of prediction for the centroid of each category. This paperconstructs a matrix of "Customer Lifecycle Theory - Five Elements of Marketing", and puts forward corresponding marketing suggestions for customers atdifferent life cycle stages.

Research and application of time series algorithms in centralized purchasing data