CINDI: 条件填补与噪声数据完整性在电力网络数据中的应用
CINDI: Conditional Imputation and Noisy Data Integrity with Flows in Power Grid Data
AI总结：
CINDI通过条件归一化流统一异常检测与填补，提升电力网络数据在噪声环境下的可靠性与准确性。
摘要：
现实中的多变量时间序列，特别是在关键基础设施如电力电网中，常常被噪声和异常所破坏，从而影响下游任务的性能。标准的数据清理方法通常依赖于分散的策略，即用一个模型检测错误，用另一个模型填补缺失值。此类方法难以捕捉数据的完整联合分布，并忽略预测不确定性。本文介绍了条件填补与噪声数据完整性（CINDI），一种无监督的概率框架，旨在恢复复杂时间序列的数据完整性。与碎片化方法不同，CINDI将异常检测和填补整合到一个端到端系统中，该系统基于条件归一化流构建。通过建模数据的精确条件似然，该框架可以识别低概率段，并迭代采样统计上一致的替代值。这使CINDI能够高效地重用学习到的信息，同时保持系统底层的物理和统计特性。我们使用来自挪威电力分配运营商的真实电网损失数据评估了该框架，尽管该方法设计用于任何多变量时间序列领域。结果表明，CINDI相比竞争基线表现出稳健的性能，提供了一种在噪声环境中维护可靠性的可扩展解决方案。
英文摘要：
Real-world multivariate time series, particularly in critical infrastructure such as electrical power grids, are often corrupted by noise and anomalies that degrade the performance of downstream tasks. Standard data cleaning approaches often rely on disjoint strategies, which involve detecting errors with one model and imputing them with another. Such approaches can fail to capture the full joint distribution of the data and ignore prediction uncertainty. This work introduces Conditional Imputation and Noisy Data Integrity (CINDI), an unsupervised probabilistic framework designed to restore data integrity in complex time series. Unlike fragmented approaches, CINDI unifies anomaly detection and imputation into a single end-to-end system built on conditional normalizing flows. By modeling the exact conditional likelihood of the data, the framework identifies low-probability segments and iteratively samples statistically consistent replacements. This allows CINDI to efficiently reuse learned information while preserving the underlying physical and statistical properties of the system. We evaluate the framework using real-world grid loss data from a Norwegian power distribution operator, though the methodology is designed to generalize to any multivariate time series domain. The results demonstrate that CINDI yields robust performance compared to competitive baselines, offering a scalable solution for maintaining reliability in noisy environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
