CINDI: Conditional Imputation and Noisy Data Integrity with Flows in Power Grid Data

TL;DR

CINDI is an unsupervised probabilistic framework that unifies anomaly detection and data imputation in power grid time series using conditional normalizing flows, improving data integrity and robustness.

Contribution

This work introduces CINDI, a novel end-to-end system that models the joint distribution of multivariate time series for anomaly detection and imputation using conditional flows.

Findings

CINDI outperforms baseline methods in real-world power grid data.

The framework effectively detects anomalies and imputes missing or corrupted data.

CINDI maintains physical and statistical properties of the system.

Abstract

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.