Scholarlink

This study developed neural network models using deep learning with a range of 4 to 10 neurons to forecast the stochastic demand of a group of 20 clients over three years. The models were compared with traditional approaches such as simple moving average, weighted moving average, simple exponential smoothing, and double exponential smoothing. The results revealed that the deep learning model with 10 neurons was the most efficient, significantly reducing forecast errors compared to traditional methods and machine learning. This study highlights the ability of neural networks to capture complex patterns and adapt to stochastic variability, providing greater accuracy and consistency in demand prediction. These findings are crucial for decision making in environments with uncertain and variable demand, improving planning and resource management. Additionally, the deep learning model with 10 neurons showed an average reduction in mean absolute deviation by 15%, compared to 25% in traditional methods. This improvement in accuracy underscores the potential of neural networks in demand forecasting applications.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Neural Networks for Demand Prediction in Stochastic Environments

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Alexander Rossi,
  • Francisco Jones,
  • Edgardo Fuentes,
  • Inesmar Briceño,
  • Gustavo Gatica

摘要

This study developed neural network models using deep learning with a range of 4 to 10 neurons to forecast the stochastic demand of a group of 20 clients over three years. The models were compared with traditional approaches such as simple moving average, weighted moving average, simple exponential smoothing, and double exponential smoothing. The results revealed that the deep learning model with 10 neurons was the most efficient, significantly reducing forecast errors compared to traditional methods and machine learning. This study highlights the ability of neural networks to capture complex patterns and adapt to stochastic variability, providing greater accuracy and consistency in demand prediction. These findings are crucial for decision making in environments with uncertain and variable demand, improving planning and resource management. Additionally, the deep learning model with 10 neurons showed an average reduction in mean absolute deviation by 15%, compared to 25% in traditional methods. This improvement in accuracy underscores the potential of neural networks in demand forecasting applications.