TiSAT: Time Series Anomaly Transformer

• URL: http://arxiv.org/abs/2203.05167v1
• Date: Thu, 10 Mar 2022 05:46:58 GMT
• Title: TiSAT: Time Series Anomaly Transformer
• Authors: Keval Doshi, Shatha Abudalou and Yasin Yilmaz
• Abstract summary: We show that a rudimentary Random Guess method can outperform state-of-the-art detectors in terms of this popular but faulty evaluation criterion. In this work, we propose a proper evaluation metric that measures the timeliness and precision of detecting sequential anomalies.
• Score: 30.68108039722565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While anomaly detection in time series has been an active area of research for several years, most recent approaches employ an inadequate evaluation criterion leading to an inflated F1 score. We show that a rudimentary Random Guess method can outperform state-of-the-art detectors in terms of this popular but faulty evaluation criterion. In this work, we propose a proper evaluation metric that measures the timeliness and precision of detecting sequential anomalies. Moreover, most existing approaches are unable to capture temporal features from long sequences. Self-attention based approaches, such as transformers, have been demonstrated to be particularly efficient in capturing long-range dependencies while being computationally efficient during training and inference. We also propose an efficient transformer approach for anomaly detection in time series and extensively evaluate our proposed approach on several popular benchmark datasets.

Related papers

• Towards Unbiased Evaluation of Time-series Anomaly Detector [6.521243384420707]
  Time series anomaly detection (TSAD) is an evolving area of research motivated by its critical applications. In this work, we propose an alternative adjustment protocol called Balanced point adjustment'' (BA)
  arXiv  Detail & Related papers  (2024-09-19T19:02:45Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Algorithmic Recourse for Anomaly Detection in Multivariate Time Series [19.74694026053318]
  We propose an algorithmic recourse framework, called RecAD, which can recommend recourse actions to flip the abnormal time steps. Experiments on two synthetic and one real-world datasets show the effectiveness of our framework.
  arXiv  Detail & Related papers  (2023-09-28T23:50:11Z)
• 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)
• Time-Series Anomaly Detection with Implicit Neural Representation [0.38073142980733]
  Implicit Neural Representation-based Anomaly Detection (INRAD) is proposed. We train a simple multi-layer perceptron that takes time as input and outputs corresponding values at that time. Then we utilize the representation error as an anomaly score for detecting anomalies.
  arXiv  Detail & Related papers  (2022-01-28T06:17:24Z)
• An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series [7.675917669905486]
  This paper presents a systematic and comprehensive evaluation of unsupervised and semi-supervised deep-learning based methods for anomaly detection and diagnosis. We vary the model and post-processing of model errors, through a grid of 10 models and 4 scoring functions, comparing these variants to state of the art methods. We find that the existing evaluation metrics either do not take events into account, or cannot distinguish between a good detector and trivial detectors.
  arXiv  Detail & Related papers  (2021-09-23T15:14:24Z)
• Optimal Sequential Detection of Signals with Unknown Appearance and Disappearance Points in Time [64.26593350748401]
  The paper addresses a sequential changepoint detection problem, assuming that the duration of change may be finite and unknown. We focus on a reliable maximin change detection criterion of maximizing the minimal probability of detection in a given time (or space) window. The FMA algorithm is applied to detecting faint streaks of satellites in optical images.
  arXiv  Detail & Related papers  (2021-02-02T04:58:57Z)
• 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)
• Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation [69.34035527763916]
  Change point detection (CPD) aims to locate abrupt property changes in time series data. Recent CPD methods demonstrated the potential of using deep learning techniques, but often lack the ability to identify more subtle changes in the autocorrelation statistics of the signal. We employ an autoencoder-based methodology with a novel loss function, through which the used autoencoders learn a partially time-invariant representation that is tailored for CPD.
  arXiv  Detail & Related papers  (2020-08-21T15:03:21Z)
• Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time [93.6788993843846]
  We propose a novel time-varying Bayesian optimization algorithm that can effectively handle the non-constant evaluation time. Our bound elucidates that a pattern of the evaluation time sequence can hugely affect the difficulty of the problem.
  arXiv  Detail & Related papers  (2020-03-10T13:28:33Z)
• Sequential Adversarial Anomaly Detection for One-Class Event Data [18.577418448786634]
  We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available. We propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator.
  arXiv  Detail & Related papers  (2019-10-21T06:12:47Z)

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.