Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models

• URL: http://arxiv.org/abs/2007.15541v1
• Date: Thu, 30 Jul 2020 15:48:55 GMT
• Title: Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
• Authors: Fadhel Ayed, Lorenzo Stella, Tim Januschowski, Jan Gasthaus
• Abstract summary: Main novelty in our approach is that instead of modeling time series consisting of real values or vectors of real values, we model time series of probability distributions over real values (or vectors) Our method is amenable to streaming anomaly detection and scales to monitoring for anomalies on millions of time series. We show that we outperform popular open-source anomaly detection tools by up to 17% average improvement for a real-world data set.
• Score: 14.621700495712647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a new methodology for detecting anomalies in time series data, with a primary application to monitoring the health of (micro-) services and cloud resources. The main novelty in our approach is that instead of modeling time series consisting of real values or vectors of real values, we model time series of probability distributions over real values (or vectors). This extension to time series of probability distributions allows the technique to be applied to the common scenario where the data is generated by requests coming in to a service, which is then aggregated at a fixed temporal frequency. Our method is amenable to streaming anomaly detection and scales to monitoring for anomalies on millions of time series. We show the superior accuracy of our method on synthetic and public real-world data. On the Yahoo Webscope data set, we outperform the state of the art in 3 out of 4 data sets and we show that we outperform popular open-source anomaly detection tools by up to 17% average improvement for a real-world data set.

Related papers

• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• 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)
• LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection [49.52429991848581]
  We propose a Light and Anti-overfitting Retraining Approach (LARA) for deep variational auto-encoder based time series anomaly detection methods (VAEs) This work aims to make three novel contributions: 1) the retraining process is formulated as a convex problem and can converge at a fast rate as well as prevent overfitting; 2) designing a ruminate block, which leverages the historical data without the need to store them; and 3) mathematically proving that when fine-tuning the latent vector and reconstructed data, the linear formations can achieve the least adjusting errors between the ground truths and the fine-tuned ones.
  arXiv  Detail & Related papers  (2023-10-09T12:36:16Z)
• Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time [69.77704012415845]
  Temporal shifts can considerably degrade performance of machine learning models deployed in the real world. We benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning. Under both evaluation strategies, we observe an average performance drop of 20% from in-distribution to out-of-distribution data.
  arXiv  Detail & Related papers  (2022-11-25T17:07:53Z)
• Monte Carlo EM for Deep Time Series Anomaly Detection [6.312089019297173]
  Time series data are often corrupted by outliers or other kinds of anomalies. Recent approaches to anomaly detection and forecasting assume that the proportion of anomalies in the training data is small enough to ignore. We present a technique for augmenting existing time series models so that they explicitly account for anomalies in the training data.
  arXiv  Detail & Related papers  (2021-12-29T07:52:36Z)
• Neural Contextual Anomaly Detection for Time Series [7.523820334642732]
  We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series. NCAD scales seamlessly from the unsupervised to supervised setting. We demonstrate empirically on standard benchmark datasets that our approach obtains a state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-07-16T04:33:53Z)
• Deep Autoregressive Models with Spectral Attention [74.08846528440024]
  We propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module. By characterizing in the spectral domain the embedding of the time series as occurrences of a random process, our method can identify global trends and seasonality patterns. Two spectral attention models, global and local to the time series, integrate this information within the forecast and perform spectral filtering to remove time series's noise.
  arXiv  Detail & Related papers  (2021-07-13T11:08:47Z)
• SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series [1.0437764544103274]
  This paper introduces SALAD, which is a Self-Adaptive Lightweight Anomaly Detection approach based on a special type of recurrent neural networks called Long Short-Term Memory (LSTM) Experiments based on two real-world open-source time series datasets demonstrate that SALAD outperforms five other state-of-the-art anomaly detection approaches in terms of detection accuracy. In addition, the results also show that SALAD is lightweight and can be deployed on a commodity machine.
  arXiv  Detail & Related papers  (2021-04-19T10:36:23Z)
• 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.