CINDI: Conditional Imputation and Noisy Data Integrity with Flows in Power Grid Data
arXiv:2603.11745v1 Announce Type: new 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 replacem
arXiv:2603.11745v1 Announce Type: new 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.
Executive Summary
The article introduces CINDI, an unsupervised probabilistic framework designed to address noise and anomalies in multivariate time series data, particularly in critical infrastructure like power grids. CINDI unifies anomaly detection and imputation through conditional normalizing flows, offering a more holistic approach than disjoint strategies. By modeling conditional likelihood and iteratively sampling consistent replacements, it preserves underlying physical and statistical properties while improving data integrity. Evaluated on real-world grid loss data, CINDI demonstrates robust performance against baselines, suggesting scalability and applicability across domains. The work addresses a critical gap in data cleaning methodologies by integrating detection and imputation into a unified probabilistic framework.
Key Points
- ▸ Unified framework for anomaly detection and imputation
- ▸ Use of conditional normalizing flows for conditional likelihood modeling
- ▸ Evaluation on real-world power grid data with generalizable applicability
Merits
Holistic Integration
CINDI’s unification of detection and imputation via conditional flows eliminates fragmentation and better captures joint data distributions, improving accuracy and reliability.
Scalability
The framework’s design supports application across diverse multivariate time series domains without customization, enhancing practical utility.
Demerits
Assumption Dependence
CINDI relies on the availability of sufficient data quality signals to identify low-probability segments; in highly corrupted or sparse datasets, this may limit effectiveness without additional calibration.
Implementation Complexity
Conditional normalizing flows, while powerful, introduce computational overhead that may hinder deployment in resource-constrained environments.
Expert Commentary
CINDI represents a significant advancement in the field of time series data integrity by bridging a long-standing divide between detection and imputation techniques. The authors effectively leverage conditional normalizing flows—a sophisticated yet interpretable statistical tool—to create an end-to-end solution that respects the underlying probabilistic structure of the data. Unlike traditional approaches that treat errors as isolated anomalies, CINDI’s iterative sampling mechanism aligns with Bayesian inference principles, allowing the model to propagate uncertainty in a principled manner. The evaluation on Norwegian grid data is a strong validation point, though broader generalization claims warrant further testing across heterogeneous grid architectures and non-European data sources. Moreover, the framework’s success hinges on the assumption that the conditional likelihood can be meaningfully estimated; in cases where model calibration is uncertain (e.g., due to sensor drift or cyber-physical interference), supplementary validation layers may be necessary. Overall, CINDI offers a compelling shift toward integrated, probabilistic data cleaning—a model that may influence both academic research and practical infrastructure management.
Recommendations
- ✓ Researchers should extend CINDI’s evaluation to include synthetic fault injection scenarios to better assess robustness under adversarial or extreme noise conditions.
- ✓ Industry stakeholders should pilot CINDI in controlled grid environments to evaluate real-time performance and computational impact before full-scale deployment.