Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows
arXiv:2603.11756v1 Announce Type: new Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing data likelihood. However, likelihood in observation space measures marginal density rather than conformity to structured temporal dynamics, and therefore can assign high probability to anomalous or out-of-distribution samples. We address this structural limitation by relocating the notion of anomaly to a prescribed latent space. We introduce explicit inductive biases in conditional normalizing flows, modeling time-series observations within a discrete-time state-space framework that constrains latent representations to evolve according to prescribed temporal dynamics. Under this formulation, expected behavior corresponds to compliance with a specified distribution over latent trajectories, while anomalies are defined as violations of these dynamics. Anomaly detection is consequently reduced to a statistically grounded compliance t
arXiv:2603.11756v1 Announce Type: new Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing data likelihood. However, likelihood in observation space measures marginal density rather than conformity to structured temporal dynamics, and therefore can assign high probability to anomalous or out-of-distribution samples. We address this structural limitation by relocating the notion of anomaly to a prescribed latent space. We introduce explicit inductive biases in conditional normalizing flows, modeling time-series observations within a discrete-time state-space framework that constrains latent representations to evolve according to prescribed temporal dynamics. Under this formulation, expected behavior corresponds to compliance with a specified distribution over latent trajectories, while anomalies are defined as violations of these dynamics. Anomaly detection is consequently reduced to a statistically grounded compliance test, such that observations are mapped to latent space and evaluated via goodness-of-fit tests against the prescribed latent evolution. This yields a principled decision rule that remains effective even in regions of high observation likelihood. Experiments on synthetic and real-world time-series demonstrate reliable detection of anomalies in frequency, amplitude, and observation noise, while providing interpretable diagnostics of model compliance.
Executive Summary
This article introduces a novel approach to anomaly detection in time-series data using conditional normalizing flows with inductive biases in the latent space. By relocating the notion of anomaly to a prescribed latent space, the authors address the limitation of traditional likelihood-based methods, which can assign high probability to anomalous samples. The proposed method models time-series observations within a discrete-time state-space framework, allowing for the detection of anomalies as violations of prescribed temporal dynamics. Experiments demonstrate reliable detection of anomalies in various scenarios, providing interpretable diagnostics of model compliance.
Key Points
- ▸ Introduction of inductive biases in conditional normalizing flows for anomaly detection
- ▸ Relocation of anomaly notion to a prescribed latent space
- ▸ Modeling time-series observations within a discrete-time state-space framework
Merits
Robust Anomaly Detection
The proposed method can detect anomalies in regions of high observation likelihood, where traditional methods may fail.
Interpretable Diagnostics
The method provides interpretable diagnostics of model compliance, allowing for a deeper understanding of the detected anomalies.
Demerits
Complexity of Latent Space
The choice of latent space and the prescribed temporal dynamics may require careful tuning and expertise, potentially limiting the method's applicability.
Expert Commentary
The proposed method represents a significant advancement in anomaly detection for time-series data, as it addresses the limitations of traditional likelihood-based approaches. By introducing inductive biases in the latent space, the authors provide a principled decision rule that can detect anomalies in a statistically grounded manner. However, the complexity of the latent space and the choice of prescribed temporal dynamics require careful consideration. Future research should focus on developing more efficient and scalable methods for selecting the optimal latent space and dynamics, as well as exploring applications in various domains.
Recommendations
- ✓ Further research on the selection of optimal latent space and prescribed temporal dynamics
- ✓ Exploration of applications in various domains, such as finance, healthcare, and cybersecurity.