CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching

• URL: http://arxiv.org/abs/2410.12261v2
• Date: Fri, 14 Feb 2025 06:59:46 GMT
• Title: CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching
• Authors: Xingjian Wu, Xiangfei Qiu, Zhengyu Li, Yihang Wang, Jilin Hu, Chenjuan Guo, Hui Xiong, Bin Yang,
• Abstract summary: We introduce CATCH, a framework based on frequency patching. We propose a Channel Fusion Module (CFM), which features a patch-wise mask generator and a masked-attention mechanism. Experiments on 10 real-world datasets and 12 synthetic datasets demonstrate that CATCH achieves state-of-the-art performance.
• Score: 24.927390742543707
• License:
• Abstract: Anomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur. Reconstruction-based methods, which focus on learning normal patterns in the frequency domain to detect diverse abnormal subsequences, achieve promising results, while still falling short on capturing fine-grained frequency characteristics and channel correlations. To contend with the limitations, we introduce CATCH, a framework based on frequency patching. We propose to patchify the frequency domain into frequency bands, which enhances its ability to capture fine-grained frequency characteristics. To perceive appropriate channel correlations, we propose a Channel Fusion Module (CFM), which features a patch-wise mask generator and a masked-attention mechanism. Driven by a bi-level multi-objective optimization algorithm, the CFM is encouraged to iteratively discover appropriate patch-wise channel correlations, and to cluster relevant channels while isolating adverse effects from irrelevant channels. Extensive experiments on 10 real-world datasets and 12 synthetic datasets demonstrate that CATCH achieves state-of-the-art performance. We make our code and datasets available at https://github.com/decisionintelligence/CATCH.

Related papers

• F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain Information [10.113418621891281]
  We propose a new time series anomaly detection method called F-SE-LSTM. This method utilizes two sliding windows and fast Fourier transform (FFT) to construct a frequency matrix. We show that F-SE-LSTM exhibits better discriminative ability than ordinary time domain and frequency domain data.
  arXiv  Detail & Related papers  (2024-12-03T14:36:24Z)
• Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
  Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings. In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities. We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
  arXiv  Detail & Related papers  (2024-07-19T13:33:38Z)
• Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformer [18.459822172890473]
  We introduce a frequency-aware attention module to unweave skeleton frequency representations. We also develop a mixed transformer architecture to incorporate spatial features with frequency features. Experiments show that FreqMiXFormer outperforms SOTA on 3 popular skeleton recognition datasets.
  arXiv  Detail & Related papers  (2024-07-17T05:47:27Z)
• Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning [81.98675881423131]
  This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. We introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors.
  arXiv  Detail & Related papers  (2024-03-12T01:28:00Z)
• MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction [6.980076213134384]
  MultiWave is a novel framework that enhances deep learning time series models by incorporating components that operate at the intrinsic frequencies of signals. We show that MultiWave consistently identifies critical features and their frequency components, thus providing valuable insights into the applications studied.
  arXiv  Detail & Related papers  (2023-06-16T20:07:15Z)
• Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems [74.52117784544758]
  This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
  arXiv  Detail & Related papers  (2023-06-08T06:15:17Z)
• Adaptive Frequency Learning in Two-branch Face Forgery Detection [66.91715092251258]
  We propose Adaptively learn Frequency information in the two-branch Detection framework, dubbed AFD. We liberate our network from the fixed frequency transforms, and achieve better performance with our data- and task-dependent transform layers.
  arXiv  Detail & Related papers  (2022-03-27T14:25:52Z)
• Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection [11.926787216956459]
  We propose a novel Omni-frequency Channel-selection Reconstruction (OCR-GAN) network to handle anomaly detection task in a perspective of frequency. We show that our approach markedly surpasses the reconstruction-based baseline by +38.1 and the current SOTA method by +0.3.
  arXiv  Detail & Related papers  (2022-03-01T06:35:15Z)
• F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams [53.70940420595329]
  We propose F-FADE, a new approach for detection of anomalies in edge streams. It uses a novel frequency-factorization technique to efficiently model the time-evolving distributions of frequencies of interactions between node-pairs. F-FADE is able to handle in an online streaming setting a broad variety of anomalies with temporal and structural changes, while requiring only constant memory.
  arXiv  Detail & Related papers  (2020-11-09T19:55:40Z)
• 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.