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

• URL: http://arxiv.org/abs/2603.11745v1
• Date: Thu, 12 Mar 2026 09:49:10 GMT
• Title: CINDI: Conditional Imputation and Noisy Data Integrity with Flows in Power Grid Data
• Abstract summary: Conditional Imputation and Noisy Data Integrity (CINDI) is an unsupervised probabilistic framework designed to restore data integrity in complex time series.<n>CINDI unifies anomaly detection and imputation into a single end-to-end system built on conditional normalizing flows.<n>We evaluate the framework using real-world grid loss data from a Norwegian power distribution operator.
• Score: 0.6991281327290525
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• TimeLAVA: Learning-Agnostic Data Valuation for Time Series [48.76711740818533]
  TimeLAVA is a learning-agnostic framework that values temporal segments by their marginal contribution to minimize distributional discrepancy between evaluated and reference data.<n>We provide theoretical guarantees linking valuation to model-agnostic generalization and prove bounded sensitivity to contamination.
  arXiv  Detail & Related papers  (2026-06-17T06:16:16Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [65.30332997607141]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion [7.021277706390712]
  Missing intemporal systems presents a challenge for modern applications ranging from environmental monitoring to urban traffic management.<n>Current approaches based on machine learning and deep learning struggle to model thedependencies between spatial and temporal dimensions effectively.<n>CoFILL builds on the inherent advantages of diffusion-quality models to generate high-quality imputations.
  arXiv  Detail & Related papers  (2025-06-08T11:53:06Z)
• Stress-Testing ML Pipelines with Adversarial Data Corruption [11.91482648083998]
  Regulators now demand evidence that high-stakes systems can withstand realistic, interdependent errors.<n>We introduce SAVAGE, a framework that formally models data-quality issues through dependency graphs and flexible corruption templates.<n>Savanage employs a bi-level optimization approach to efficiently identify vulnerable data subpopulations and fine-tune corruption severity.
  arXiv  Detail & Related papers  (2025-06-02T00:41:24Z)
• Geometric Median Matching for Robust k-Subset Selection from Noisy Data [75.86423267723728]
  We propose a novel k-subset selection strategy that leverages Geometric Median -- a robust estimator with an optimal breakdown point of 1/2.<n>Our method iteratively selects a k-subset such that the mean of the subset approximates the GM of the (potentially) noisy dataset, ensuring robustness even under arbitrary corruption.
  arXiv  Detail & Related papers  (2025-04-01T09:22:05Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation for Time Series [45.76310830281876]
  We propose Quantile Sub-Ensembles, a novel method to estimate uncertainty with ensemble of quantile-regression-based task networks. Our method not only produces accurate imputations that is robust to high missing rates, but also is computationally efficient due to the fast training of its non-generative model.
  arXiv  Detail & Related papers  (2023-12-03T05:52:30Z)
• Measuring and Mitigating Local Instability in Deep Neural Networks [23.342675028217762]
  We study how the predictions of a model change, even when it is retrained on the same data, as a consequence of principledity in the training process. For Natural Language Understanding (NLU) tasks, we find instability in predictions for a significant fraction of queries. We propose new data-centric methods that exploit our local stability estimates.
  arXiv  Detail & Related papers  (2023-05-18T00:34:15Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.