A Rolling-Window Framework for Churn Prediction and Behavioral Driver Identification

• URL: http://arxiv.org/abs/2606.06776v1
• Date: Thu, 04 Jun 2026 23:38:43 GMT
• Title: A Rolling-Window Framework for Churn Prediction and Behavioral Driver Identification
• Abstract summary: This study proposes a temporally explicit churn prediction framework that models customer behavior using rolling behavioral windows.<n>The framework integrates feature-based and sequence-based learning approaches within a unified temporal design.<n> Empirical results demonstrate strong and stable predictive performance, with accuracy reaching 87.6% and ROC-AUC of 0.94 for the feature-based model.
• Score: 0.9449650062296823
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Customer churn prediction is a central task in customer analytics, particularly in non-contractual, pay-per-use service environments where disengagement is not explicitly observed and must be inferred from behavioral inactivity. Existing churn prediction approaches often rely on simplified temporal assumptions or single-point representations of customer behavior, which limit their ability to support continuous risk assessment, interpretability, and realistic deployment over time. This study proposes a temporally explicit churn prediction framework that models customer behavior using rolling behavioral windows, enabling repeated and instance-level churn risk estimation as customer activity evolves. Customer behavior is summarized within a fixed 30-day observation window, followed by a 30-day future churn evaluation window, ensuring a clear temporal separation between behavioral evidence and churn outcomes. The framework integrates feature-based and sequence-based learning approaches within a unified temporal design. The proposed approach is evaluated on a large-scale, real-world dataset from a non-contractual service platform. Empirical results demonstrate strong and stable predictive performance, with accuracy reaching 87.6% and ROC-AUC of 0.94 for the feature-based model, while the sequence-based model achieves recall as high as 96.1% by capturing temporal disengagement patterns. Evaluation on future unseen data confirms meaningful robustness under temporal shift, with accuracy remaining above 83% and ROC-AUC exceeding 0.91 without model retraining. Overall, the findings highlight that carefully designed temporal framing, rather than model complexity alone, is critical for achieving robust, interpretable, and deployment-ready churn prediction. The study provides a practical foundation for churn-oriented decision support in dynamic service environments.

Related papers

• DTD-VAE: Disentangled Temporal Dependencies VAE for Credit Risk Prediction [12.699895244790246]
  We introduce the Disentangled Temporal Dependencies Variational Autoencoder (DTD-VAE)<n>It is designed to disentangle temporal dependencies and distinguish credit risk-related features from past customer preferences.<n>It consistently outperforms existing methods, achieving performance gains of 3.2%-4.86% in ROC-AUC and 6.41%-9.71% in Accuracy Ratio.
  arXiv  Detail & Related papers  (2026-08-27T00:02:52Z)
• BehaviorBench: Modeling Real-World User Decisions from Behavioral Traces [53.55407626944686]
  textscBehaviorBench is a benchmark for evaluating personalized decision modeling from real-world behavioral traces.<n>textscBehaviorBench reconstructs wallet-level decision histories from observed public prediction-market and on-chain records.<n>Across 2,000 evaluation wallets, the benchmark contains 141,445 Belief instances and 1,485,972 Trade instances.
  arXiv  Detail & Related papers  (2026-06-01T19:04:36Z)
• Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting [49.05788441962762]
  We argue for agentic time series forecasting (ATSF), which reframes forecasting as an agentic process composed of perception, planning, action, reflection, and memory.<n>We outline three representative implementation paradigms -- workflow-based design, agentic reinforcement learning, and a hybrid agentic workflow paradigm -- and discuss the opportunities and challenges that arise when shifting from model-centric prediction to agentic forecasting.
  arXiv  Detail & Related papers  (2026-02-02T08:01:11Z)
• FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning [0.6524460254566904]
  Federated learning (FL) enables collaborative model training while preserving data privacy.<n>It remains vulnerable to malicious clients who compromise model integrity through Byzantine attacks, data poisoning, or adaptive adversarial behaviors.<n>We propose FLARE, an adaptive reputation-based framework that transforms client reliability assessment from binary decisions to a continuous, multi-dimensional trust evaluation.
  arXiv  Detail & Related papers  (2025-11-18T17:57:40Z)
• Counterfactual Forecasting For Panel Data [7.191006213124838]
  We address the challenge of forecasting counterfactual outcomes in a panel data with missing entries and temporally dependent latent factors.<n>We propose Forecasting Counterfactuals under Dynamics (FOCUS), a method that extends traditional matrix completion methods.
  arXiv  Detail & Related papers  (2025-11-09T02:25:49Z)
• Uncertainty-boosted Robust Video Activity Anticipation [72.14155465769201]
  Video activity anticipation aims to predict what will happen in the future, embracing a broad application prospect ranging from robot vision to autonomous driving. Despite the recent progress, the data uncertainty issue, reflected as the content evolution process and dynamic correlation in event labels, has been somehow ignored. We propose an uncertainty-boosted robust video activity anticipation framework, which generates uncertainty values to indicate the credibility of the anticipation results.
  arXiv  Detail & Related papers  (2024-04-29T12:31:38Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• Improving Adaptive Conformal Prediction Using Self-Supervised Learning [72.2614468437919]
  We train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores. We empirically demonstrate the benefit of the additional information using both synthetic and real data on the efficiency (width), deficit, and excess of conformal prediction intervals.
  arXiv  Detail & Related papers  (2023-02-23T18:57:14Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)
• On the estimation of discrete choice models to capture irrational customer behaviors [4.683806391173103]
  We show how to use partially-ranked preferences to efficiently model rational and irrational customer types from transaction data. An extensive set of experiments assesses the predictive accuracy of the proposed approach.
  arXiv  Detail & Related papers  (2021-09-08T19:19:51Z)
• Interpretable Machine Learning Approaches to Prediction of Chronic Homelessness [2.294014185517203]
  We introduce a machine learning approach to predict chronic homelessness from de-identified client shelter records. Our model, HIFIS-RNN-MLP, incorporates both static and dynamic features of a client's history to forecast chronic homelessness 6 months into the client's future.
  arXiv  Detail & Related papers  (2020-09-12T15:02:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.