Why do zeroes happen? A model-based approach for demand classification

• URL: http://arxiv.org/abs/2504.05894v1
• Date: Tue, 08 Apr 2025 10:45:30 GMT
• Title: Why do zeroes happen? A model-based approach for demand classification
• Authors: Ivan Svetunkov, Anna Sroginis,
• Abstract summary: We propose a two-stage model-based classification framework that identifies artificially occurring zeroes.<n>We then argue that different types of demand need different features, and show empirically that they tend to increase the accuracy of the forecasting methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Effective demand forecasting is critical for inventory management, production planning, and decision making across industries. Selecting the appropriate model and suitable features to efficiently capture patterns in the data is one of the main challenges in demand forecasting. In reality, this becomes even more complicated when the recorded sales have zeroes, which can happen naturally or due to some anomalies, such as stockouts and recording errors. Mistreating the zeroes can lead to the application of inappropriate forecasting methods, and thus leading to poor decision making. Furthermore, the demand itself can have different fundamental characteristics, and being able to distinguish one type from another might bring substantial benefits in terms of accuracy and thus decision making. We propose a two-stage model-based classification framework that in the first step, identifies artificially occurring zeroes, and then classifies demand to one of the possible types: regular/intermittent, intermittent smooth/lumpy, fractional/count. The framework utilises statistical modelling and information criteria to detect anomalous zeroes and then classify demand into those categories. We then argue that different types of demand need different features, and show empirically that they tend to increase the accuracy of the forecasting methods compared to those applied directly to the dataset without the generated features and the two-stage framework. Our general practical recommendation based on that is to use the mixture approach for intermittent demand, capturing the demand sizes and demand probability separately, as it seems to improve the accuracy of different forecasting approaches.

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