Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection

2026-04-19

AI Summary

Existing methods for root cause analysis in time series anomaly detection often rely on unrealistic feature perturbations and neglect temporal dynamics and cross-variable dependencies, leading to unreliable attributions. This work proposes a conditional attribution framework that identifies root causes by retrieving normal-state contexts similar to the anomalous context while preserving dependency structures, thereby establishing a more faithful baseline for attribution. The approach innovatively combines the latent space of a variational autoencoder with UMAP manifold embeddings to enable efficient, high-fidelity low-dimensional context retrieval, effectively avoiding out-of-distribution artifacts. Furthermore, it incorporates confidence-aware mechanisms and temporal evaluation metrics to enhance attribution reliability. Evaluated on the SWaT and MSDS benchmarks, the method substantially outperforms existing approaches, achieving significant improvements in root cause identification accuracy, temporal localization precision, and cross-model robustness.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Time-Series/Data Streams
• Reasoning under Uncertainty: Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems. Existing explanation methods often rely on unrealistic feature perturbations and ignore temporal and cross-feature dependencies, leading to unreliable attributions. We propose a conditional attribution framework that explains anomalies relative to contextually similar normal system states. Instead of using marginal or randomly sampled baselines, our method retrieves representative normal instances conditioned on the anomalous observation, enabling dependency-preserving and operationally meaningful explanations. To support high-dimensional time-series data, contextual retrieval is performed in learned low-dimensional representations using both variational autoencoder latent spaces and UMAP manifold embeddings. By grounding the retrieval process in the system's learned manifold, this strategy avoids out-of-distribution artifacts and ensures attribution fidelity while maintaining computational efficiency. We further introduce confidence-aware and temporal evaluation metrics for assessing explanation reliability and responsiveness. Experiments on the SWaT and MSDS benchmarks demonstrate that the proposed approach consistently improves root-cause identification accuracy, temporal localization, and robustness across multiple anomaly detection models. These results highlight the practical utility of conditional attribution for explainable anomaly diagnosis in complex time-series systems. Code and models will be publicly released.

Problem

• Root Cause Analysis
• Time-Series Anomaly Detection
• Feature Dependencies
• Attribution
• Explanation Reliability

Innovation

• conditional attribution
• root cause analysis
• time-series anomaly detection
• contextual retrieval
• dependency-preserving explanation