CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition
cs.LG
Submitted: 2026-08-26
Updated: 2026-08-26
Comments: 12 pages, 4 figures, 5 tables. Published in KDD 2026
Journal ref: Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD '26), August 09-13, 2026, Jeju Island, Republic of Korea. ACM, 2026, 12 pages
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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,
Terminology
Abstract
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.
Sources
- Decision Transformer: Reinforcement Learning via Sequence Modeling
- Multi-Task Temporal Fusion Transformer for Joint Sales and Inventory Forecasting in Amazon E-Commerce Supply Chain
- Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
- iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
- Turning Semantics into Topology: LLM-Driven Attribute Augmentation for Collaborative Filtering
- A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
- Temporal Fusion Transformer for Multi-Horizon Probabilistic Forecasting of Weekly Retail Sales
- ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
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