Cold-Start Forecasting of New Product Life-Cycles via Conditional Diffusion Models

TL;DR

This paper introduces CDLF, a conditional generative model that forecasts new product life-cycle trajectories during cold-start phases by integrating static descriptors, reference trajectories, and new observations, improving accuracy and uncertainty quantification.

Contribution

The paper presents a novel conditional diffusion framework for cold-start product forecasting that adaptively updates predictions without retraining and outperforms existing methods.

Findings

CDLF achieves more accurate point forecasts than classical models.

It provides higher-quality probabilistic forecasts in cold-start scenarios.

The method maintains consistency with a distributional error bound.

Abstract

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.