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

• URL: http://arxiv.org/abs/2604.20370v1
• Date: Wed, 22 Apr 2026 09:07:55 GMT
• Title: Cold-Start Forecasting of New Product Life-Cycles via Conditional Diffusion Models
• Abstract summary: Conditional Diffusion Life-cycle Forecaster (CDLF) is a conditional generative framework for forecasting new-product life-cycle trajectories under cold start.<n>It combines three sources of information: static descriptors, reference trajectories from similar products, and newly arriving observations.
• Score: 4.245074043309951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning [0.764671395172401]
  Current approaches primarily focus on forecasting, while real-world time series are often irregularly and partially observed.<n>We introduce TS-ICL, a novel probabilistic In-Context Learning encoder--regressor Transformer that unifies forecasting and imputation.
  arXiv  Detail & Related papers  (2026-06-04T08:52:21Z)
• Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables [59.37476887007663]
  We develop ApolloPFN, a prior-data fitted network (PFN) that is time-aware (unlike prior PFNs)<n>Our design introduces two major advances: (i) a synthetic data generation procedure tailored to resolve the failure modes that arise when PFNs are applied to time series; and (ii) time-aware architectural modifications that embed inductive biases needed to exploit the time series context.
  arXiv  Detail & Related papers  (2026-03-16T18:33:23Z)
• TSB-HB: A Hierarchical Bayesian Extension of the TSB Model for Intermittent Demand Forecasting [0.8594140167290097]
  Intermittent demand forecasting poses unique challenges due to sparse observations, cold-start items, and obsolescence.<n>Deep learning models address these limitations but often require large datasets and sacrifice interpretability.<n>We introduce TSB-HB, a hierarchical Bayesian extension of TSB. Demand occurrence is modeled with a Beta-Binomial distribution, while nonzero demand sizes follow a Log-Normal distribution.<n>This framework yields a fully generative and interpretable model that generalizes classical exponential smoothing.
  arXiv  Detail & Related papers  (2025-11-16T19:24:33Z)
• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting [52.6508222408558]
  We introduce Elucidated Rolling Diffusion Models (ERDM)<n>ERDM is the first framework to unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM)<n>On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5circ resolution, ERDM consistently outperforms key diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-06-24T21:44:31Z)
• Sundial: A Family of Highly Capable Time Series Foundation Models [47.27032162475962]
  We introduce Sundial, a family of native, flexible, and scalable time series foundation models.<n>Our models are pre-trained without specifying any prior distribution and can generate multiple probable predictions.<n>Sundial achieves state-of-the-art results on both point and probabilistic forecasting benchmarks with a just-in-time inference speed.
  arXiv  Detail & Related papers  (2025-02-02T14:52:50Z)
• LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data [63.777637042161544]
  This paper introduces a novel forecast post-processor that fine-tunes large language models to incorporate unstructured semantic and contextual information and historical data.<n>In an industry-scale retail application, we demonstrate that our technique yields statistically significantly forecast improvements across several sets of products subject to holiday-driven demand surges.
  arXiv  Detail & Related papers  (2024-12-03T16:18:42Z)
• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting [43.951394031702016]
  We introduce TSFlow, a conditional flow matching (CFM) model for time series combining Gaussian processes, optimal transport paths, and data-dependent prior distributions.<n>We show that both conditionally and unconditionally trained models achieve competitive results across multiple forecasting benchmarks.
  arXiv  Detail & Related papers  (2024-10-03T22:12:50Z)
• Sequential Recommendation via Stochastic Self-Attention [68.52192964559829]
  Transformer-based approaches embed items as vectors and use dot-product self-attention to measure the relationship between items. We propose a novel textbfSTOchastic textbfSelf-textbfAttention(STOSA) to overcome these issues. We devise a novel Wasserstein Self-Attention module to characterize item-item position-wise relationships in sequences.
  arXiv  Detail & Related papers  (2022-01-16T12:38:45Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• Intermittent Demand Forecasting with Renewal Processes [16.079036729678716]
  We introduce a new, unified framework for building intermittent demand forecasting models. Our framework is based on extensions of well-established model-based methods to discrete-time renewal processes. We report predictive accuracy in a variety of scenarios that compares favorably to the state of the art.
  arXiv  Detail & Related papers  (2020-10-04T11:32:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.