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.
@article{arxiv.2508.15369,
title = {Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based Data},
author = {Yonathan Guttel and Orit Moradov and Nachi Lieder and Asnat Greenstein-Messica},
journal= {arXiv preprint arXiv:2508.15369},
year = {2025}
}