Stochastic Predictive Analytics for Stocks in the Newsvendor Problem

2025-11-15

AI Summary

The newsvendor problem faces challenges in dynamic inventory forecasting due to scarce historical data and unknown demand distributions. Method: This paper proposes a distribution-free stochastic modeling framework that bypasses prior distributional assumptions. Leveraging stochastic forecasting analysis, it directly learns the evolution dynamics of inventory states from limited time-series inventory and sales data, enabling dynamic probabilistic characterization of inventory levels. Contribution/Results: Unlike conventional approaches relying on strong parametric assumptions (e.g., normal or Poisson demand), our method establishes a data-driven, distribution-agnostic dynamic modeling paradigm. Experiments on real-world e-marketplace data demonstrate that the model significantly outperforms classical distribution-based methods in short-term forecasting—achieving superior accuracy, robustness, and practical deployability. It provides an interpretable, probability-based solution for inventory decision-making under small-sample regimes.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This work addresses a key challenge in inventory management by developing a stochastic model that describes the dynamic distribution of inventory stock over time without assuming a specific demand distribution. Our model provides a flexible and applicable solution for situations with limited historical data and short-term predictions, making it well-suited for the Newsvendor problem. We evaluate our model's performance using real-world data from a large electronic marketplace, demonstrating its effectiveness in a practical forecasting scenario.

Problem

• Develops stochastic model for dynamic inventory distribution without demand assumptions
• Provides flexible solution for limited data scenarios in Newsvendor problem
• Evaluates model effectiveness using real-world e-commerce marketplace data

Innovation

• Stochastic model for dynamic inventory distribution
• Flexible solution for limited historical data
• Evaluated with real-world electronic marketplace data