CEDAR：基于残差分解的可控事件驱动需求预测
CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition
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中文总结 AI 辅助
该研究针对现有时间序列预测方法对策略不敏感、反事实分析不可靠的问题，提出基于残差分解的两阶段框架CEDAR，在阿里1688数据集上验证其可提升模拟精度并助力预算规划。
中文摘要 AI 辅助
大规模电商市场中的预测日益需要为规划提供支持：商家需要评估在未来行动序列（如预算安排）下的销售结果，而非仅被动预测后续发生的情况。然而，大多数现有的时间序列预测（TSF）方法本质上仍是被动的，即便将运营决策作为辅助协变量纳入，它们通常也会在历史策略下优化基于相关性的外推。这种设计存在自回归惯性问题，且将内生市场演化与决策引发的转变混为一谈，导致对策略不敏感的推演和不可靠的反事实分析。为弥合这一差距，我们提出了CEDAR（基于感知行动的残差分解的可控事件驱动需求预测），这是一个用于鲁棒决策条件模拟的两阶段框架。在第一阶段，行动交织Transformer学习可控的行动条件状态转换，以在计划干预下进行推演；在第二阶段，残差校正模块利用外部事件信号和大语言模型（LLM）辅助的文本表示，将噪声事件描述与产品上下文对齐，并校正事件驱动的偏差。本研究依托阿里巴巴1688提供的大规模真实数据集，该数据集包含约3200万条产品轨迹，以及配对的状态-行动序列和对齐的事件信号。广泛的离线实验和生产环境中的在线对照实验表明，CEDAR相比强大的TSF基线始终提升了模拟精度，并为真实世界的预算规划带来了实际收益。
英文摘要
Forecasting in large-scale e-commerce marketplaces is increasingly required to support planning: merchants need to evaluate sales outcomes under future action sequences such as budget schedules, rather than passively predicting what happens next. However, most existing time series forecasting (TSF) approaches remain inherently passive. Even when incorporating operational decisions as auxiliary covariates, they typically optimize for correlation-based extrapolation under historical policies. This design suffers from autoregressive inertia and conflates endogenous market evolution with decision-induced transitions, leading to policy-insensitive rollouts and unreliable counterfactual analysis. To bridge this gap, we propose CEDAR (Controlled and Event-Driven Demand forecasting via Action-aware Residual decomposition), a two-stage framework for robust decision-conditioned simulation. In Stage I, an Action-Interleaved Transformer learns controllable action-conditioned state transitions for rollout under planned interventions. In Stage II, a Residual Correction Module leverages external event signals and LLM-assisted text representations to align noisy event descriptions with product context and correct event-driven deviations. Our study is enabled by a large-scale real-world dataset from Alibaba 1688, comprising approximately 32 million product trajectories with paired state-action sequences and aligned event signals. Extensive offline experiments and online controlled experiments in production demonstrate that CEDAR consistently improves simulation accuracy over strong TSF baselines and delivers practical gains for real-world budget planning.
发表机构
- School of Artificial Intelligence and Data Science, University of Science and Technology of China（中国科学技术大学人工智能与数据科学学院）
- Alibaba Group（阿里巴巴集团）
- Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能学域）
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