MIXAD: Memory-Induced Explainable Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2410.22735v1
• Date: Wed, 30 Oct 2024 06:46:23 GMT
• Title: MIXAD: Memory-Induced Explainable Time Series Anomaly Detection
• Authors: Minha Kim, Kishor Kumar Bhaumik, Amin Ahsan Ali, Simon S. Woo,
• Abstract summary: We introduce MIXAD (Memory Explainable Time Series Atemporally Detection), a model designed for interpretable anomaly detection. We also introduce a novel anomaly scoring method that detects significant shifts in memory activation patterns during anomalies. Our approach not only ensures decent detection performance but also outperforms state-of-the-art baselines by 34.30% and 34.51% in interpretability metrics.
• Score: 21.208134038200203
• License:
• Abstract: For modern industrial applications, accurately detecting and diagnosing anomalies in multivariate time series data is essential. Despite such need, most state-of-the-art methods often prioritize detection performance over model interpretability. Addressing this gap, we introduce MIXAD (Memory-Induced Explainable Time Series Anomaly Detection), a model designed for interpretable anomaly detection. MIXAD leverages a memory network alongside spatiotemporal processing units to understand the intricate dynamics and topological structures inherent in sensor relationships. We also introduce a novel anomaly scoring method that detects significant shifts in memory activation patterns during anomalies. Our approach not only ensures decent detection performance but also outperforms state-of-the-art baselines by 34.30% and 34.51% in interpretability metrics.

Related papers

• Pattern-Based Time-Series Risk Scoring for Anomaly Detection and Alert Filtering -- A Predictive Maintenance Case Study [3.508168174653255]
  We propose a fast and efficient approach to anomaly detection and alert filtering based on sequential pattern similarities. We show how this approach can be leveraged for a variety of purposes involving anomaly detection on a large scale real-world industrial system.
  arXiv  Detail & Related papers  (2024-05-24T20:27:45Z)
• 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)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection [37.992737349167676]
  We propose a multi-normal-pattern accommodated anomaly detection method in the frequency domain for time series anomaly detection. There are three novel characteristics of it: (i) a pattern extraction mechanism excelling at handling diverse normal patterns with a unified model; (ii) a dualistic convolution mechanism that amplifies short-term anomalies in the time domain and hinders the reconstruction of anomalies in the frequency domain; and (iii) leveraging the sparsity and parallelism of frequency domain to enhance model efficiency.
  arXiv  Detail & Related papers  (2023-11-26T03:31:43Z)
• ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection [44.21198064126152]
  We propose a novel anomaly detection framework named ImDiffusion. ImDiffusion combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets.
  arXiv  Detail & Related papers  (2023-07-03T04:57:40Z)
• Time series anomaly detection with reconstruction-based state-space models [10.085100442558828]
  We propose a novel unsupervised anomaly detection method for time series data. A long short-term memory (LSTM)-based encoder-decoder is adopted to represent the mapping between the observation space and the latent space. Regularization of the latent space places constraints on the states of normal samples, and Mahalanobis distance is used to evaluate the abnormality level.
  arXiv  Detail & Related papers  (2023-03-06T17:52:35Z)
• HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection [2.253268952202213]
  We propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS. We first combine the embedding similarity subgraph generated by sensor embedding and feature value similarity subgraph generated by sensor values to construct a time-series heterogeneous graph. This approach fuses the state-of-the-art technologies of heterogeneous graph structure learning (HGSL) and representation learning.
  arXiv  Detail & Related papers  (2022-11-01T05:01:34Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Object-centric and memory-guided normality reconstruction for video anomaly detection [56.64792194894702]
  This paper addresses anomaly detection problem for videosurveillance. Due to the inherent rarity and heterogeneity of abnormal events, the problem is viewed as a normality modeling strategy. Our model learns object-centric normal patterns without seeing anomalous samples during training.
  arXiv  Detail & Related papers  (2022-03-07T19:28:39Z)
• 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)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

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.