针对真实场景的复杂缺损数据，提出动态填补框架DIM-SUM，提升预测精度与效率。
DIM-SUM: Dynamic IMputation for Smart Utility Management
- 基于模式聚类与自适应掩码，模拟真实缺失模式训练模型
- 在超20亿条加州水电气数据上，用更少数据达更高精度
- 相比大模型快2倍，适合工业级实时监控系统
时间序列填补模型传统上使用人工掩码的完整数据集进行训练。然而在真实基础设施监控中，数据常出现大量缺失且模式复杂多变。我们提出DIM-SUM，一种用于训练鲁棒填补模型的预处理框架，弥合了人工掩码数据与真实缺失模式之间的差距。DIM-SUM结合模式聚类与自适应掩码策略，并具备理论学习保证，可处理实际数据中观察到的多样化缺失模式。在来自加州水务区、电力数据集及基准测试的超20亿条读数上进行的广泛实验表明，与传统方法相比，DIM-SUM在更低的处理时间和更少训练数据下达到相近甚至更高的准确率；相较于大型预训练模型，其平均精度高出2倍，推理时间显著更短。
原文摘要 · Abstract (English)
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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