  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.
