利用神经网络和宏观经济指标进行产品需求预测:产品类别比较研究

Tuan Ngoc Nguyen, Mahfuz Haider, Afjal Hossain Jisan, Md Azad Hossain Raju, Touhid Imam, Md Munsur Khan, Abdullah Evna Jafar
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引用次数: 0

摘要

在竞争激烈的全球企业领域,零售业需求预测的复杂性已成为一个焦点。以往的研究虽然深入探讨了各种方法,但始终忽略了不同零售产品类别中预测模型的不同表现。了解这些预测性能的差异至关重要,可帮助企业针对每个类别对预测模型进行微调。本研究通过仔细研究针对不同产品类别的模型的预测性能,弥补了这一不足。在近期研究的基础上,我们将消费者物价指数、消费者情绪指数和失业率等外部宏观经济指标与不同类别零售额的时间序列数据结合起来。我们利用这一合并数据集来训练长短期记忆模型,预测不同产品类别的未来需求。我们通过识别最有助于解释产品需求的特征并量化其强度,进一步扩展了分析。拟合模型可全面了解其性能,并确定需要重点开发模型的产品类别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Product Demand Forecasting with Neural Networks and Macroeconomic Indicators: A Comparative Study among Product Categories
In the fiercely competitive global corporate arena, the intricacies of demand forecasting in the retail sector have become a focal point. While previous research has delved into various methodologies, it consistently overlooks the distinct performances of forecasting models within different retail product categories. Understanding these variations in prediction performances is pivotal, enabling firms to fine-tune forecasting models for each category. This study bridges this gap by scrutinizing the prediction performances of models tailored to different product categories. Building on recent research, we incorporate external macroeconomic indicators like the Consumer Price Index, Consumer Sentiment Index, and unemployment rate, alongside time series data of retail sales spanning various categories. This amalgamated dataset is employed to train a Long Short Term Memory model, projecting future demand across product categories. We further extend the analysis by identifying features that contribute most towards explaining product demand and quantifying their strength. The fitted models yield comprehensive insights into their performances and pinpoint the product categories warranting more focused model development.
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