{"ID":2879759,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.15369","arxiv_id":"2508.15369","title":"Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based Data","abstract":"This paper introduces a novel two-dimensional (2D) time series forecasting model that integrates cohort behavior over time, addressing challenges in small data environments. We demonstrate its efficacy using multiple real-world datasets, showcasing superior performance in accuracy and adaptability compared to reference models. The approach offers valuable insights for strategic decision-making across industries facing financial and marketing forecasting challenges.","short_abstract":"This paper introduces a novel two-dimensional (2D) time series forecasting model that integrates cohort behavior over time, addressing challenges in small data environments. We demonstrate its efficacy using multiple real-world datasets, showcasing superior performance in accuracy and adaptability compared to reference...","url_abs":"https://arxiv.org/abs/2508.15369","url_pdf":"https://arxiv.org/pdf/2508.15369v1","authors":"[\"Yonathan Guttel\",\"Orit Moradov\",\"Nachi Lieder\",\"Asnat Greenstein-Messica\"]","published":"2025-08-21T08:53:40Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}