![]() The results show that a specific product needs to be represented by a dedicated model. In the empirical study, actual tray sales data and a comparison of different models that combine missing data processing methods and forecasters are employed. In this article, two propositions are presented: (1) a dedicated time series forecasting scheme, which is both accurate and sustainable, and (2) a practical observation of the data background to deal with the problem of missing data and to effectively formulate correction strategies after predictions. To deal with high market uncertainty, companies need a reliable and sustainable forecasting mechanism. ![]() If not appropriately addressed, it usually leads to failed modeling and distorted forecasting. The problem of missing data is frequently met in time series analysis.
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