TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis

• URL: http://arxiv.org/abs/2210.09693v2
• Date: Sat, 25 Mar 2023 06:26:36 GMT
• Title: TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis
• Authors: Chaoli Zhang and Tian Zhou and Qingsong Wen and Liang Sun
• Abstract summary: Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. We propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD, to exploit both time and frequency domains for performance improvement.
• Score: 12.867257563413972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and deep models have been proposed, most of them mainly focus on time-domain modeling, and do not fully utilize the information in the frequency domain of the time series data. In this paper, we propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD for short, to exploit both time and frequency domains for performance improvement. Besides, we incorporate time series decomposition and data augmentation mechanisms in the designed time-frequency architecture to further boost the abilities of performance and interpretability. Empirical studies on widely used benchmark datasets show that our approach obtains state-of-the-art performance in univariate and multivariate time series anomaly detection tasks. Code is provided at https://github.com/DAMO-DI-ML/CIKM22-TFAD.

Related papers

• Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis [31.43159668073136]
  Unsupervised anomaly detection in time series is essential in industrial applications, as it significantly reduces the need for manual intervention. Traditional methods use Graph Neural Networks (GNNs) or Transformers to analyze spatial while RNNs to model temporal dependencies. This paper introduces a novel temporal model built on an enhanced Graph Attention Network (GAT) for multivariate time series anomaly detection called TopoGDN.
  arXiv  Detail & Related papers  (2024-08-23T14:06:30Z)
• Deep Time Series Models: A Comprehensive Survey and Benchmark [74.28364194333447]
  Time series data is of great significance in real-world scenarios. Recent years have witnessed remarkable breakthroughs in the time series community. We release Time Series Library (TSLib) as a fair benchmark of deep time series models for diverse analysis tasks.
  arXiv  Detail & Related papers  (2024-07-18T08:31:55Z)
• 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)
• HyperTime: Implicit Neural Representation for Time Series [131.57172578210256]
  Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. In this paper, we analyze the representation of time series using INRs, comparing different activation functions in terms of reconstruction accuracy and training convergence speed. We propose a hypernetwork architecture that leverages INRs to learn a compressed latent representation of an entire time series dataset.
  arXiv  Detail & Related papers  (2022-08-11T14:05:51Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection [3.772827533440353]
  Real-world time-series data exhibit complex temporal dependencies. RAE-M EPC learns informative normal representations based on multi-resolution ensemble and predictive coding. Experiments on real-world benchmark datasets show that the proposed model outperforms the benchmark models.
  arXiv  Detail & Related papers  (2022-02-21T05:47:22Z)
• PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series [5.911865723926626]
  Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. We show that PIETS is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.
  arXiv  Detail & Related papers  (2021-09-30T20:01:19Z)
• 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)
• HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions [7.016615391171876]
  HIFI builds multivariate feature interaction graph automatically and uses the graph convolutional neural network to achieve high-order feature interactions. Experiments on three publicly available datasets demonstrate the superiority of our framework compared with state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-06-11T04:57:03Z)
• 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)
• RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks [37.16594704493679]
  We propose RobustTAD, a Robust Time series Anomaly Detection framework. It integrates robust seasonal-trend decomposition and convolutional neural network for time series data. It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.
  arXiv  Detail & Related papers  (2020-02-21T20:43:45Z)

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.