标题： DIM-SUM：动态插补用于智能公用事业管理 显示英文标题

标题： DIM-SUM: Dynamic IMputation for Smart Utility Management

摘要： 时间序列插补模型传统上是使用带有人工掩码模式的完整数据集来开发的，以模拟缺失值。 然而，在现实世界中的基础设施监测中，从业者经常遇到大量数据缺失且遵循复杂、异构模式的数据集。 我们引入了DIM-SUM，这是一种用于训练鲁棒插补模型的预处理框架，弥合了人工掩码训练数据与真实缺失模式之间的差距。 DIM-SUM结合了模式聚类和自适应掩码策略，并具有理论学习保证，以处理实际数据中观察到的多样化缺失模式。 通过在加利福尼亚州水务区、电力数据集和基准测试的超过20亿条记录上的广泛实验，我们证明DIM-SUM在达到相似精度的同时，处理时间更短，所需训练数据显著减少，因此优于传统方法。 与一个大型预训练模型相比，DIM-SUM的平均准确率高出2倍，且推理时间显著减少。 显示英文摘要

摘要： 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.