Research and application of time series algorithms in centralized
  purchasing data
  Based on the online transaction data of COSCO group's centralized procurement
platform, this paper studies the clustering method of time series type data.
The different methods of similarity calculation, different clustering methods
with different K values are analysed, and the best clustering method suitable
for centralized purchasing data is determined. The company list under the
corresponding cluster is obtained. The time series motif discovery algorithm is
used to model the centroid of each cluster. Through ARIMA method, we also made
12 periods of prediction for the centroid of each category. This paper
constructs a matrix of "Customer Lifecycle Theory - Five Elements of Marketing
", and puts forward corresponding marketing suggestions for customers at
different life cycle stages.
