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This paper introduces the Conditional Diffusion Life-cycle Forecaster (CDLF), a novel framework designed to predict the life-cycle trajectories of newly launched products during the cold-start phase when data is scarce. By leveraging static product descriptors, reference trajectories from similar products, and incoming observations, CDLF adapts its forecasts dynamically without the need for retraining, leading to improved accuracy in both point and probabilistic forecasts. The results demonstrate that CDLF significantly outperforms traditional diffusion models and other advanced machine-learning techniques in forecasting accuracy across various case studies, including Intel microprocessor SKUs and open large language model repositories.
CDLF outperforms traditional forecasting methods by adapting to new product data in real-time, even in the absence of historical outcomes.
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.
