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

2026-03-12

AI Summary

This work addresses the challenge of noise and anomalies in multivariate time series from critical infrastructure such as power systems, which degrade downstream task performance. It proposes the first unified unsupervised probabilistic framework that jointly performs anomaly detection and missing value imputation. The method leverages conditional normalizing flows to model the conditional likelihood of observed data, identifying low-probability segments as anomalies and iteratively sampling statistically consistent replacements while preserving both physical and statistical properties of the system. Experiments on real-world Norwegian power grid loss data demonstrate that the approach outperforms existing baselines in robustness and scalability, and effectively quantifies predictive uncertainty.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

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.

Problem

• multivariate time series
• anomaly detection
• data imputation
• noisy data
• power grid data

Innovation

• conditional normalizing flows
• anomaly detection
• data imputation
• multivariate time series
• unsupervised probabilistic modeling