DIM-SUM: Dynamic IMputation for Smart Utility Management

• URL: http://arxiv.org/abs/2506.20023v1
• Date: Tue, 24 Jun 2025 21:38:06 GMT
• Title: DIM-SUM: Dynamic IMputation for Smart Utility Management
• Authors: Ryan Hildebrant, Rahul Bhope, Sharad Mehrotra, Christopher Tull, Nalini Venkatasubramanian,
• Abstract summary: We introduce DIM-SUM, a preprocessing framework for training robust imputation models.<n>DIM-SUM bridges the gap between artificially masked training data and real missing patterns.<n>We demonstrate that DIM-SUM outperforms traditional methods by reaching similar accuracy with lower processing time and significantly less training data.
• Score: 4.494470981739729
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Time series imputation models have traditionally been developed using complete datasets with artificial masking patterns to simulate missing values. However, in real-world infrastructure monitoring, practitioners often encounter datasets where large amounts of data are missing and follow complex, heterogeneous patterns. We introduce DIM-SUM, a preprocessing framework for training robust imputation models that bridges the gap between artificially masked training data and real missing patterns. DIM-SUM combines pattern clustering and adaptive masking strategies with theoretical learning guarantees to handle diverse missing patterns actually observed in the data. Through extensive experiments on over 2 billion readings from California water districts, electricity datasets, and benchmarks, we demonstrate that DIM-SUM outperforms traditional methods by reaching similar accuracy with lower processing time and significantly less training data. When compared against a large pre-trained model, DIM-SUM averages 2x higher accuracy with significantly less inference time.

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