发表机构
Florida State University（佛罗里达州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对效用数据缺失问题，提出多视图行为感知扩散模型MBDiff，通过多视图用户行为提取模块和行为感知条件扩散模型，在佛罗里达州市政数据集上优于现有基线模型，提升了用电和用水块缺失插补性能。
AI 中文摘要
由无处不在的传感器和嵌入式设备收集的效用数据（如电、水、燃气消耗数据）常因设备故障、数据传输问题等因素存在大量缺失值，这会严重影响效用计费准确性、阻碍需求预测、扰乱高效的效用供应管理，因此效用数据插补受到了工业界和学术界的广泛关注。尽管已有诸多研究尝试解决该问题，但多数研究依赖聚合数据集进行训练，忽略了可用于提升插补准确性的丰富用户行为信息；且从长期、多样且不完整的效用数据中学习全面的用户行为仍是重大挑战，同时利用用户行为信息指导插补因关联的间接性而并非易事。为应对这些挑战，本文提出MBDiff，即用于概率效用数据插补的多视图行为感知扩散模型。MBDiff包含两个关键技术组件：（i）多视图用户行为提取模块，可从全局、局部、实例级等多个视角学习全面的用户行为；（ii）行为感知条件扩散模型，由参考选择模块和条件注意力去噪网络组成，能以计算高效的方式对效用数据进行插补。本文与佛罗里达州最大的市政效用供应商之一合作，对MBDiff进行实现与评估，实验结果表明，所提出的MBDiff有效优于现有最先进的基线模型，例如，在块缺失插补任务中，其在用电量数据集上提升了7.04%，在用水量数据集上提升了29.1%。
英文摘要
Utility data (e.g., electricity, water, and gas consumption), collected by ubiquitous sensors and embedded devices, often contains substantial missing values due to various factors such as device failures and data transmission issues. The data missingness can severely impact utility billing accuracy, hinder demand forecasting, and disrupt efficient utility supply management. As a result, utility data imputation has attracted much interest from both industry and academia. While many studies have attempted to address this issue, most of them rely on aggregated datasets for training, overlooking rich user behavior information, which could provide valuable insights for more accurate imputation. However, learning comprehensive user behavior from long-term, diverse, and incomplete utility data remains a significant challenge. Moreover, leveraging user behavior information to guide imputation is nontrivial due to the indirect nature of the correlations. To address these challenges, we propose MBDiff, a Multi-view Behavior-aware Diffusion Model for Probabilistic Utility Data Imputation. MBDiff incorporates two key technical components: (i) a multi-view User Behavior Extraction module that learns comprehensive user behavior from multiple perspectives, including global, local, and instance-level views; and (ii) a behavior-aware conditional diffusion model consisting of a reference selection module and a conditional attentional denoising network to impute utility data in a computationally efficient manner. We implement and evaluate MBDiff by collaborating with one of the largest municipal utility providers in Florida. Experimental results demonstrate our proposed MBDiff effectively outperforms state-of-the-art baselines, e.g., it improves 7.04% and 29.1% on the electricity and water usage datasets for block missingness imputation, respectively.
