Accurate demand forecasting in the retail industry is a critical determinant of financial performance and supply chain efficiency. As global markets become increasingly interconnected, businesses are turning towards advanced prediction models to gain a competitive edge. However, existing literature mostly focuses on historical sales data and ignores the vital influence of macroeconomic conditions on consumer spending behavior. In this study, we bridge this gap by enriching time series data of customer demand with macroeconomic variables, such as the Consumer Price Index (CPI), Index of Consumer Sentiment (ICS), and unemployment rates. Leveraging this comprehensive dataset, we develop and compare various regression and machine learning models to predict retail demand accurately.

翻译：零售行业中的准确需求预测是决定财务绩效与供应链效率的关键因素。随着全球市场日益互联，企业正转向先进预测模型以获取竞争优势。然而，现有文献主要聚焦于历史销售数据，忽略了宏观经济状况对消费者支出行为的重要影响。在本研究中，我们通过将客户需求时间序列数据与消费者价格指数（CPI）、消费者信心指数（ICS）及失业率等宏观经济变量相结合，填补了这一研究空白。基于这一综合数据集，我们开发并比较了多种回归模型与机器学习模型，以准确预测零售需求。