用二维时间序列建模用户群随时间变化，提升小数据下预测精度。
Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based Data
- 将用户群体行为建模为二维时间序列，捕捉跨群体与时间的动态关系
- 在多个真实数据集上优于基准模型，准确率与适应性显著提升
- 适合金融、营销等需小样本精准预测的场景
本文提出一种新型二维（2D）时间序列预测模型，通过整合用户群随时间演变的行为特征，解决小数据环境下的预测挑战。我们在多个真实世界数据集上验证该方法的有效性，结果表明其在准确性与适应性方面均优于基准模型。该方法可为面临财务与营销预测难题的行业提供决策支持。
原文摘要 · Abstract (English)
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.
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