Fast and Accurate Anomaly Detection in Time Series

• URL: http://arxiv.org/abs/2607.02046v1
• Date: Thu, 02 Jul 2026 11:14:51 GMT
• Title: Fast and Accurate Anomaly Detection in Time Series
• Abstract summary: We introduce a novel unsupervised algorithm for anomaly detection in time series based on the Haar discrete wavelet.<n>We demonstrate that our algorithm outperforms state-of-the-art unsupervised and self-supervised benchmarks.
• Score: 3.332793247280083
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Anomaly detection is a critical and evolving field in Machine Learning, with applications targeting different domains such as cybersecurity, finance, healthcare, manufacturing and IoT (Internet of Things) systems. Traditionally, anomaly detection algorithms have been designed using both supervised and unsupervised learning paradigms. The fundamental challenge in real-world anomaly detection scenarios is related to the inherent class imbalance (anomalies are typically rare) and, for supervised methods, to the scarcity of labelled anomalous data. Indeed, labelling is both expensive and time-consuming. Conversely unsupervised methods do not require labelling, but may suffer from high false positive rates when deployed in safety-critical applications. In this work we introduce a novel unsupervised algorithm for anomaly detection in time series based on the Haar discrete wavelet and a suitably designed $t$-test. We establish the theoretical foundation of the proposed $t$-test and, through extensive experimentation across 343 datasets, demonstrate that our algorithm outperforms state-of-the-art unsupervised and self-supervised benchmarks.

Related papers

• Learning Unified Representations of Normalcy for Time Series Anomaly Detection [0.0]
  In this paper, we present a novel framework, Unified Unsupervised Anomaly Detection ($textU2textAD$)<n>Our approach learns the underlying data distribution of normal samples by utilizing score-based generative modeling.<n>Our experimental evaluations demonstrate that $textU2textAD$ not only outperforms current state-of-the-art methods in detection accuracy but also identifies anomalies at significantly earlier stages of their occurrence.
  arXiv  Detail & Related papers  (2026-05-10T18:12:22Z)
• Universal Transformation of One-Class Classifiers for Unsupervised Anomaly Detection [51.73001988341294]
  Anomaly detection is typically formulated as a one-class classification problem.<n>We present a dataset folding method that transforms an arbitrary one-class classifier-based anomaly detector into a fully unsupervised method.
  arXiv  Detail & Related papers  (2026-02-13T16:54:12Z)
• Labels Matter More Than Models: Quantifying the Benefit of Supervised Time Series Anomaly Detection [56.302586730134806]
  Time series anomaly detection (TSAD) is a critical data mining task often constrained by label scarcity.<n>Current research predominantly focuses on Unsupervised Time-series Anomaly Detection.<n>This paper challenges the premise that architectural complexity is the optimal path for TSAD.
  arXiv  Detail & Related papers  (2025-11-20T08:32:49Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [89.92916473403108]
  This paper proposes a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods.<n>The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics.<n>We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection [53.83593870825628]
  One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learning the normal behaviour of unlabelled time series in an unsupervised manner. We introduce a novel end-to-end self-supervised ContrAstive Representation Learning approach for time series anomaly detection.
  arXiv  Detail & Related papers  (2023-08-18T04:45:56Z)
• Precursor-of-Anomaly Detection for Irregular Time Series [31.73234935455713]
  We present a novel type of anomaly detection, called Precursor-of-Anomaly (PoA) detection. To solve both problems at the same time, we present a neural controlled differential equation-based neural network and its multi-task learning algorithm.
  arXiv  Detail & Related papers  (2023-06-27T14:10:09Z)
• PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
  We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
  arXiv  Detail & Related papers  (2023-01-25T16:34:43Z)
• Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability [21.98595908296989]
  We demonstrate that the performance of state-of-the-art anomaly detection methods is degraded substantially by adding only small adversarial perturbations to the sensor data. We use different scoring metrics such as prediction errors, anomaly, and classification scores over several public and private datasets. We demonstrate, for the first time, the vulnerabilities of anomaly detection systems against adversarial attacks.
  arXiv  Detail & Related papers  (2022-08-24T01:55:50Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention [0.0]
  This paper proposes an unsupervised anomaly detection method based on a Variational Recurrent Autoencoder with attention mechanism. With "dirty" data from smart meters, our method pre-detects missing values and global anomalies to shrink their contribution while training.
  arXiv  Detail & Related papers  (2022-06-08T19:39:51Z)
• Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators [0.0]
  Anomaly detection is a challenging task for machine learning algorithms due to the inherent class imbalance. Inspired by generative models and the analysis of the hidden activations of neural networks, we introduce a novel unsupervised anomaly detection method called DA3D.
  arXiv  Detail & Related papers  (2021-01-12T18:07:34Z)
• 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.