Thomas Gaertner, Christoph Lippert, Stefan Konigorski
arXiv 29 Dec 2024 · Econometrics
arXiv:2412.20420 · PDF · DOI · OpenAlex · Extracted main text
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 with dedicated data scientists for sales forecasting, SMEs often lack such resources. To address this, we developed a comprehensive forecasting pipeline that automates time series sales forecasting, encompassing data preparation, model training, and selection based on validation results. The development included two main components: model preselection and the forecasting pipeline. In the first phase, state-of-the-art methods were evaluated on a showcase dataset, leading to the selection of ARIMA, SARIMAX, Holt-Winters Exponential Smoothing, Regression Tree, Dilated Convolutional Neural Networks, and Generalized Additive Models. An ensemble prediction of these models was also included. Long-Short-Term Memory (LSTM) networks were excluded due to suboptimal prediction accuracy, and Facebook Prophet was omitted for compatibility reasons. In the second phase, the proposed forecasting pipeline was tested with SMEs in the food and electric industries, revealing variable model performance across different companies. While one project-based company derived no benefit, others achieved superior forecasts compared to naive estimators. Our findings suggest that no single model is universally superior. Instead, a diverse set of models, when integrated within an automated validation framework, can significantly enhance forecasting accuracy for SMEs. These results emphasize the importance of model diversity and automated validation in addressing the unique needs of each business. This research contributes to the field by providing SMEs access to state-of-the-art sales forecasting tools, enabling data-driven decision-making and improving operational efficiency.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Marco Alfonse Hassan El Madany (2022) Procurement Forecasting in Enterprise Resource and Planning (ERP) System using Hybrid Time Series Model | 0.843 | 3 | 3 | 100% |
| 2 | Sean J. Taylor and Benjamin Letham (2017) Forecasting at scale | 0.843 | 3 | 3 | 100% |
| 3 | Houssainy El, Amal Mohamed, and Haitham Fawzy (2021) Time Series Forecasting Using Tree Based Methods | 0.737 | 3 | 2 | 100% |
| 4 | Anastasia Borovykh, Sander Bohte, and Cornelis W. Oosterlee (2018) Dilated convolutional neural networks for time series forecasting | 0.644 | 2 | 2 | 100% |
| 5 | Coşkun Hamzaçebi, Diyar Akay, and Fevzi Kutay (2009) Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting | 0.644 | 2 | 2 | 100% |
| 6 | Sepp Hochreiter and Jürgen Schmidhuber Long Short-term Memory | 0.644 | 2 | 2 | 100% |
| 7 | Irena Koprinska, Dengsong Wu, and Zheng Wang (2018) Convolutional Neural Networks for Energy Time Series Forecasting | 0.644 | 2 | 2 | 100% |
| 8 | Navneet Vairagade, Doina Logofatu, Florin Leon, and Fitore Muharemi (2019) Demand Forecasting Using Random Forest and Artificial Neural Network for Supply Chain Management | 0.644 | 2 | 2 | 100% |
| 9 | G.E.P. Box and G.M. Jenkins (1970) Time Series Analysis: Forecasting and Control | 0.405 | 1 | 1 | 100% |
| 10 | R.G. Brown (1956) Exponential Smoothing for Predicting Demand | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 22 scored citations.