Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting
This work addresses the need for accurate customer loyalty and demand planning for cloud computing providers and practitioners, though it is incremental in applying TDA to time series and clusterwise regression.
The paper tackles the problem of user segmentation and demand forecasting by developing a system that combines Topological Data Analysis (TDA)-based clustering for time series with clusterwise regression using matrix factorization, achieving significantly higher accuracy than a state-of-the-art baseline on commercial datasets.
Topological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure. Its use for time series data has been limited. In this work, a system developed for a leading provider of cloud computing combining both user segmentation and demand forecasting is presented. It consists of a TDA-based clustering method for time series inspired by a popular managerial framework for customer segmentation and extended to the case of clusterwise regression using matrix factorization methods to forecast demand. Increasing customer loyalty and producing accurate forecasts remain active topics of discussion both for researchers and managers. Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level with significantly higher accuracy than a state of the art baseline. This work thus seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner.