CoCAI: Copula-based Conformal Anomaly Identification for Multivariate Time-Series

TL;DR

CoCAI introduces a copula-based conformal framework leveraging generative AI for accurate multivariate time-series forecasting and robust anomaly detection, validated on water system data.

Contribution

The paper presents a novel copula-based conformal anomaly detection framework that combines diffusion models and calibration techniques for multivariate time-series analysis.

Findings

Effective in forecasting complex dependencies in data

Robust anomaly detection with statistically valid confidence levels

Validated on real water system data

Abstract

We propose a novel framework that harnesses the power of generative artificial intelligence and copula-based modeling to address two critical challenges in multivariate time-series analysis: delivering accurate predictions and enabling robust anomaly detection. Our method, Copula-based Conformal Anomaly Identification for Multivariate Time-Series (CoCAI), leverages a diffusion-based model to capture complex dependencies within the data, enabling high quality forecasting. The model's outputs are further calibrated using a conformal prediction technique, yielding predictive regions which are statistically valid, i.e., cover the true target values with a desired confidence level. Starting from these calibrated forecasts, robust outlier detection is performed by combining dimensionality reduction techniques with copula-based modeling, providing a statistically grounded anomaly score. CoCAI benefits from an offline calibration phase that allows for minimal overhead during deployment and delivers actionable results rooted in established theoretical foundations. Empirical tests conducted on real operational data derived from water distribution and sewerage systems confirm CoCAI's effectiveness in accurately forecasting target sequences of data and in identifying anomalous segments within them.