Abstract
This paper proposes a structured, integrative, and auditable methodology for managing missing values in decision support systems (DSS) to improve forecasting reliability. It advocates a shift away from black-box imputation toward a structured workflow that integrates data preprocessing, duplicate detection, multi-faceted missingness analysis, multicriteria selection of imputation strategies, and hold-out evaluation using the same forecast metric optimized by the DSS. The workflow is validated through an extensive study of 172 residential electricity consumption time series. The results demonstrate that formal missingness analysis is essential, as non-random missing patterns identified in all tested series preclude the use of biased, simplistic models. On this single dataset, simpler task-aligned imputation methods outperformed Stable Diffusion 2 generative inpainting by more than 25 percentage points in symmetric mean absolute percentage error (sMAPE), which is a cautionary observation rather than a general recommendation. By providing a clear and transparent path from raw data to validated forecasts, the proposed approach might enhance DSS robustness to missing data.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.102
Task-Aligned Methodology for Handling Missing Data in Decision Support Systems: Workflow and Energy Case Study
This paper proposes a structured, integrative, and auditable methodology for managing missing values in decision support systems (DSS) to improve forecasting reliability. It advocates a shift away from black-box imputation toward a structured workflow that integrates data preprocessing, duplicate detection, multi-faceted missingness analysis, multicriteria selection of imputation strategies, and hold-out evaluation using the same forecast metric optimized by the DSS. The workflow is validated through an extensive study of 172 residential electricity consumption time series. The results demonstrate that formal missingness analysis is essential, as non-random missing patterns identified in all tested series preclude the use of biased, simplistic models. On this single dataset, simpler task-aligned imputation methods outperformed Stable Diffusion 2 generative inpainting by more than 25 percentage points in symmetric mean absolute percentage error (sMAPE), which is a cautionary observation rather than a general recommendation. By providing a clear and transparent path from raw data to validated forecasts, the proposed approach might enhance DSS robustness to missing data.
Recommended Citation
Kobiela, D., Kobiela, J., Kurowski, A. & Landowska, A.(2026). Task-Aligned Methodology for Handling Missing Data in Decision Support Systems: Workflow and Energy Case Study. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.102