CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution

• URL: http://arxiv.org/abs/2003.01412v3
• Date: Thu, 15 Oct 2020 13:12:23 GMT
• Title: CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution
• Authors: Ziling Wu, Ping Liu, Zheng Hu, Bocheng Li and Jun Wang
• Abstract summary: CRATOS is a self-adapt algorithms that extract features from time series, and then cluster series with similar features into one group. Our methods can significantly reduce the cost of development and maintenance of anomaly detection. The accuracy of the anomaly detection algorithms in this paper is 85.1%.
• Score: 12.906367105870341
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection of time series plays an important role in reliability systems engineering. However, in practical application, there is no precisely defined boundary between normal and anomalous behaviors in different application scenarios. Therefore, different anomaly detection algorithms and processes ought to be adopted for time series in different situation. Although such strategy improve the accuracy of anomaly detection, it takes a lot of time for practitioners to configure various algorithms to millions of series, which greatly increases the development and maintenance cost of anomaly detection processes. In this paper, we propose CRATOS which is a self-adapt algorithms that extract features from time series, and then cluster series with similar features into one group. For each group we utilize evolutionary algorithm to search the best anomaly detection methods and processes. Our methods can significantly reduce the cost of development and maintenance of anomaly detection. According to experiments, our clustering methods achieves the state-of-art results. The accuracy of the anomaly detection algorithms in this paper is 85.1%.

Related papers

• Understanding Time Series Anomaly State Detection through One-Class Classification [13.822504564241454]
  In this article, we try to re-understand and define the time series anomaly detection problem through one-class classification (OCC) We first use processes and hypothesis testing to strictly define the 'time series anomaly state detection problem', and its corresponding anomalies. Then, we use the time series classification dataset to construct an artificial dataset corresponding to the problem. We compile 38 anomaly detection algorithms and correct some of the algorithms to adapt to handle this problem.
  arXiv  Detail & Related papers  (2024-02-03T03:43:04Z)
• 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)
• Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution [89.16750999704969]
  Anomaly labels hinder traditional supervised models in time series anomaly detection. Various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. We propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD)
  arXiv  Detail & Related papers  (2023-11-19T05:37:18Z)
• 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)
• Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing [40.14838268469627]
  We develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant. The objective of the detection algorithm is to identify the anomalies with an accuracy exceeding the desired value.
  arXiv  Detail & Related papers  (2021-12-08T11:20:36Z)
• Machine Learning for Online Algorithm Selection under Censored Feedback [71.6879432974126]
  In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. We adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon.
  arXiv  Detail & Related papers  (2021-09-13T18:10:52Z)
• A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing [37.78306297797]
  We develop an anomaly detection algorithm that chooses the process to be observed at a given time instant. The objective of the detection algorithm is to arrive at a decision with an accuracy exceeding a desired value. Unlike prior work on this topic that has exponential complexity in the number of processes, our algorithm has computational and memory requirements that are both in the number of processes.
  arXiv  Detail & Related papers  (2021-05-12T17:46:01Z)
• 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)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis [75.64261155172856]
  survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
  arXiv  Detail & Related papers  (2020-07-06T15:20:17Z)
• Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art [0.0]
  Anomaly detection for time-series data has been an important research field for a long time. Recent years an increasing number of machine learning algorithms have been developed to detect anomalies on time-series. Researchers tried to improve these techniques using (deep) neural networks.
  arXiv  Detail & Related papers  (2020-04-01T13:22:34Z)

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.