NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detection

TL;DR

NeuCoReClass AD is a novel self-supervised framework for time series anomaly detection that combines multiple proxy tasks and neural transformation learning to improve generalization and performance across diverse benchmarks.

Contribution

It introduces a multi-task self-supervised approach with neural transformation learning, overcoming limitations of single proxy tasks and handcrafted transformations.

Findings

Consistently outperforms classical and deep-learning baselines.

Enables unsupervised characterization of different anomaly profiles.

Demonstrates robustness across diverse benchmark datasets.

Abstract

Time series anomaly detection plays a critical role in a wide range of real-world applications. Among unsupervised approaches, self-supervised learning has gained traction for modeling normal behavior without the need of labeled data. However, many existing methods rely on a single proxy task, limiting their ability to capture meaningful patterns in normal data. Moreover, they often depend on handcrafted transformations tailored specific domains, hindering their generalization accross diverse problems. To address these limitations, we introduce NeuCoReClass AD, a self-supervised multi-task time series anomaly detection framework that combines contrastive, reconstruction, and classification proxy tasks. Our method employs neural transformation learning to generate augmented views that are informative, diverse, and coherent, without requiring domain-specific knowledge. We evaluate NeuCoReClass AD across a wide range of benchmarks, demonstrating that it consistently outperforms both classical baselines and most deep-learning alternatives. Furthermore, it enables the characterization of distinct anomaly profiles in a fully unsupervised manner.