Recognizing that traditional forecasting models often rely solely on historical demand, this work investigates the potential of data-driven techniques to automatically select and integrate market indicators for improving customer demand predictions. By adopting an exploratory methodology, we integrate macroeconomic time series, such as national GDP growth, from the \textit{Eurostat} database into \textit{Neural Prophet} and \textit{SARIMAX} forecasting models. Suitable time series are automatically identified through different state-of-the-art feature selection methods and applied to sales data from our industrial partner. It could be shown that forecasts can be significantly enhanced by incorporating external information. Notably, the potential of feature selection methods stands out, especially due to their capability for automation without expert knowledge and manual selection effort. In particular, the Forward Feature Selection technique consistently yielded superior forecasting accuracy for both SARIMAX and Neural Prophet across different company sales datasets. In the comparative analysis of the errors of the selected forecasting models, namely Neural Prophet and SARIMAX, it is observed that neither model demonstrates a significant superiority over the other.

通过整合宏观经济时间序列和不同的特征选择方法，本研究探索数据驱动技术在改善顾客需求预测中的潜力。将不断自动识别的适当时间序列应用于工业合作伙伴的销售数据，并发现通过整合外部信息能够显著提升预测能力，尤其是使用前向特征选择技术能够自动化、无需专业知识和手动选择的方式提高预测准确性。在比较 SARIMAX 和 Neural Prophet 预测模型的误差分析中，未观察到任何模型显著优于另一个的情况。

通过自动整合市场指标优化销售预测