Research and application of time series algorithms in centralized purchasing data

TL;DR

This paper analyzes time series clustering and forecasting methods on centralized purchasing data from COSCO, identifying optimal techniques and providing marketing insights based on customer lifecycle stages.

Contribution

It introduces a clustering approach tailored for centralized procurement data and applies time series motif discovery and ARIMA forecasting to enhance marketing strategies.

Findings

Identified the best clustering method for procurement data

Forecasted purchasing trends using ARIMA for 12 periods

Proposed marketing strategies based on customer lifecycle stages

Abstract

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.