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.

We revisit the problem of finding small ε-separation keys introduced by Motwani and Xu (2008). In this problem, the input is a data set consisting of m-dimensional tuples {x1,x2,...,xn}. The goal is to find a small subset of coordinates that separates at least (1-ε)(n2) pairs of tuples. When n is large, they provided a fast algorithm that runs on Θ(m/ε) tuples sampled uniformly at random. We show that the sample size can be improved to Θ(m/√ε). Our algorithm also enjoys a faster running time.