Optimizing Sales Forecasts through Automated Integration of Market Indicators

TL;DR

This paper demonstrates that integrating macroeconomic indicators through automated feature selection significantly improves sales forecasting accuracy using Neural Prophet and SARIMAX models.

Contribution

It introduces an automated approach for selecting and integrating external market indicators into demand forecasting models, reducing manual effort and expert knowledge requirements.

Findings

Feature selection enhances forecast accuracy.

Forward Feature Selection outperforms other methods.

No clear superiority between Neural Prophet and SARIMAX.

Abstract

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.