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Forecast Model of the Price of a Product with a Cold Start

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Svitlana Drin

    (Orebro University, School of Business, Department of Statistics
    National University of Kyiv-Mohyla Academy, Faculty of Informatics, Department of Mathematics)

  • Nataliya Shchestyuk

    (Orebro University, School of Business, Department of Statistics
    National University of Kyiv-Mohyla Academy, Faculty of Informatics, Department of Mathematics)

Abstract

This article presents a comprehensive study on developing a predictive product pricing model using LightGBM, a machine learning method optimized for regression challenges in situations with limited historical data. It begins by detailing the core principles of LightGBM, including gradient descent, and then delves into the method’s unique features like Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). The model’s efficacy is demonstrated through a comparative analysis with XGBoost, highlighting Light-GBM’s enhanced efficiency and slight improvement in prediction accuracy. This research offers valuable insights into the application of LightGBM in developing fast and accurate product pricing models, crucial for businesses in the rapidly evolving data landscape.

Suggested Citation

  • Svitlana Drin & Nataliya Shchestyuk, 2024. "Forecast Model of the Price of a Product with a Cold Start," Springer Books, in: Marco Corazza & Frédéric Gannon & Florence Legros & Claudio Pizzi & Vincent Touzé (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 154-159, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-64273-9_26
    DOI: 10.1007/978-3-031-64273-9_26
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    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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