Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Cloud ComputingClusteringDemand ForecastingregressionTime SeriesTime Series AnalysisTopological Data AnalysisSimilar Papers 제목 키워드 기반
Novel Prediction Techniques Based on Clusterwise Linear Regression
In this paper we explore different regression models based on Clusterwise Linear Regression (CLR). CLR aims to find the partition of the data into $k$ clusters, such that linear regressions fitted to each of the clusters…
PredictionregressionFEC: Fast Euclidean Clustering for Point Cloud Segmentation
Segmentation from point cloud data is essential in many applications such as remote sensing, mobile robots, or autonomous cars. However, the point clouds captured by the 3D range sensor are commonly sparse and unstructur…
ClusteringInstance SegmentationPoint Cloud SegmentationSegmentation+1A Generalized Framework for Predictive Clustering and Optimization
Clustering is a powerful and extensively used data science tool. While clustering is generally thought of as an unsupervised learning technique, there are also supervised variations such as Spath's clusterwise regression…
Clusteringglobal-optimizationregressionBlocked Clusterwise Regression
A recent literature in econometrics models unobserved cross-sectional heterogeneity in panel data by assigning each cross-sectional unit a one-dimensional, discrete latent type. Such models have been shown to allow estim…
ClusteringEconometricsModel SelectionregressionSpiderMesh: Spatial-aware Demand-guided Recursive Meshing for RGB-T Semantic Segmentation
For semantic segmentation in urban scene understanding, RGB cameras alone often fail to capture a clear holistic topology in challenging lighting conditions. Thermal signal is an informative additional channel that can b…
Data AugmentationSegmentationSemantic SegmentationSemi-Supervised Semantic Segmentation+1