Optimizing Sales Forecasts through Automated Integration of Market Indicators
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.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionTime SeriesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Automated Demand Forecasting in small to medium-sized enterprises
In response to the growing demand for accurate demand forecasts, this research proposes a generalized automated sales forecasting pipeline tailored for small- to medium-sized enterprises (SMEs). Unlike large corporations…
Additive modelsDemand ForecastingReference Class Selection in Similarity-Based Forecasting of Sales Growth
This paper proposes a general method to handle forecasts exposed to behavioural bias by finding appropriate outside views, in our case corporate sales forecasts of analysts. The idea is to find reference classes, i.e. pe…
Hierarchical robust aggregation of sales forecasts at aggregated levels in e-commerce, based on exponential smoothing and Holt's linear trend method
We revisit the interest of classical statistical techniques for sales forecasting like exponential smoothing and extensions thereof (as Holt's linear trend method). We do so by considering ensemble forecasts, given by se…
Learning TheoryDistributional Reference Class Forecasting of Corporate Sales Growth With Multiple Reference Variables
This paper introduces an approach to reference class selection in distributional forecasting with an application to corporate sales growth rates using several co-variates as reference variables, that are implicit predict…
Dimensionality ReductionProfit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective
Choosing the technique that is the best at forecasting your data, is a problem that arises in any forecasting application. Decades of research have resulted into an enormous amount of forecasting methods that stem from s…
CoLAEconometricsTime Series Analysis