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Sales forecasting in the presence of Missing Data poses significant challenges, particularly for short time series where limited observations amplify the impact of incomplete records. This study analyzes a real-world transactional dataset (2021–2024) to predict quantities and prices for 2025. We classify missingness patterns and mechanisms (MCAR, MAR, MNAR) to inform the selection of imputation strategies. We evaluate techniques including MICE, Mean, KNN, and Linear Regression under simulated missingness rates, with KNN emerging as the most robust for the MAR mechanism. Regarding very short-term series predictions, the naive forecast Max2 (maximum of the last two observed values) outperformed moving averages. The results highlight the importance of mechanism-aware imputation and domain-tailored forecasting in sparse datasets. This work presents a practical framework for businesses to effectively utilize incomplete sales data.

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Managing Missing Data and Predictions in Short Time Series

  • 会议论文

  • 发表日期：

    2026年

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  • Francisco António,
  • Luís Cavique

摘要

Sales forecasting in the presence of Missing Data poses significant challenges, particularly for short time series where limited observations amplify the impact of incomplete records. This study analyzes a real-world transactional dataset (2021–2024) to predict quantities and prices for 2025. We classify missingness patterns and mechanisms (MCAR, MAR, MNAR) to inform the selection of imputation strategies. We evaluate techniques including MICE, Mean, KNN, and Linear Regression under simulated missingness rates, with KNN emerging as the most robust for the MAR mechanism. Regarding very short-term series predictions, the naive forecast Max2 (maximum of the last two observed values) outperformed moving averages. The results highlight the importance of mechanism-aware imputation and domain-tailored forecasting in sparse datasets. This work presents a practical framework for businesses to effectively utilize incomplete sales data.