Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting

TL;DR

This paper introduces a novel topology-based clustering and demand forecasting system for user segmentation using Topological Data Analysis, demonstrating improved accuracy over existing methods on real-world datasets.

Contribution

It develops a TDA-inspired clustering approach for time series and extends it with clusterwise regression using matrix factorization, tailored for demand forecasting.

Findings

Higher accuracy in user segmentation and demand forecasting compared to baseline methods.

Effective application of TDA to time series data in a commercial context.

Demonstrated utility on both public and proprietary datasets.

Abstract

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.