Forecasting the life-cycle trajectory of a newly launched product is important for launch planning, resource allocation, and early risk assessment. This task is especially difficult in the pre-launch and early post-launch phases, when product-specific outcome history is limited or unavailable, creating a cold-start problem. In these phases, firms must make decisions before demand patterns become reliably observable, while early signals are often sparse, noisy, and unstable We propose the Conditional Diffusion Life-cycle Forecaster (CDLF), a conditional generative framework for forecasting new-product life-cycle trajectories under cold start. CDLF combines three sources of information: static descriptors, reference trajectories from similar products, and newly arriving observations when available. Here, static descriptors refer to structured pre-launch characteristics of the product, such as category, price tier, brand or organization identity, scale, and access conditions. This structure allows the model to condition forecasts on relevant product context and to update them adaptively over time without retraining, yielding flexible multi-modal predictive distributions under extreme data scarcity. The method satisfies consistency with a horizon-uniform distributional error bound for recursive generation. Across studies on Intel microprocessor stock keeping unit (SKU) life cycles and the platform-mediated adoption of open large language model repositories, CDLF delivers more accurate point forecasts and higher-quality probabilistic forecasts than classical diffusion models, Bayesian updating approaches, and other state-of-the-art machine-learning baselines.

翻译：预测新上市产品的生命周期轨迹，对于上市规划、资源配置和早期风险评估具有重要意义。该任务在上市前和上市后初期阶段尤为困难，此时产品特定的结果历史数据有限或完全缺失，形成冷启动问题。在这些阶段，企业必须在需求模式变得可靠观察之前做出决策，而早期信号往往稀疏、嘈杂且不稳定。我们提出条件扩散生命周期预测器（CDLF），一种用于冷启动条件下新产品生命周期轨迹预测的条件生成框架。CDLF融合了三类信息：静态描述符、相似产品的参考轨迹以及新到达的观测数据（可用时）。其中，静态描述符指产品上市前结构化的特征，包括类别、价格区间、品牌或组织身份、规模及接入条件。该框架能使模型基于产品上下文信息进行条件预测，并随时间自适应更新而无需重新训练，在极端数据稀疏情况下生成灵活的多模态预测分布。该方法在递归生成过程中满足与时间均匀分布误差界的一致性约束。通过对Intel微处理器库存单位（SKU）生命周期及平台中介的大型语言模型仓库采用行为的实证研究，CDLF在点预测精度和概率预测质量上均优于经典扩散模型、贝叶斯更新方法及其他先进机器学习基线模型。