Anomalous Change Point Detection Using Probabilistic Predictive Coding

• URL: http://arxiv.org/abs/2405.15727v1
• Date: Fri, 24 May 2024 17:17:34 GMT
• Title: Anomalous Change Point Detection Using Probabilistic Predictive Coding
• Authors: Roelof G. Hup, Julian P. Merkofer, Alex A. Bhogal, Ruud J. G. van Sloun, Reinder Haakma, Rik Vullings,
• Abstract summary: We propose a deep learning-based CPD/AD method called Probabilistic Predictive Coding (PPC) PPC jointly learns to encode sequential data to low dimensional latent space representations and to predict the subsequent data representations as well as the corresponding prediction uncertainties. We demonstrate the effectiveness and adaptability of our proposed method across synthetic time series experiments, image data, and real-world magnetic resonance spectroscopic imaging data.
• Score: 13.719066883151623
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Change point detection (CPD) and anomaly detection (AD) are essential techniques in various fields to identify abrupt changes or abnormal data instances. However, existing methods are often constrained to univariate data, face scalability challenges with large datasets due to computational demands, and experience reduced performance with high-dimensional or intricate data, as well as hidden anomalies. Furthermore, they often lack interpretability and adaptability to domain-specific knowledge, which limits their versatility across different fields. In this work, we propose a deep learning-based CPD/AD method called Probabilistic Predictive Coding (PPC) that jointly learns to encode sequential data to low dimensional latent space representations and to predict the subsequent data representations as well as the corresponding prediction uncertainties. The model parameters are optimized with maximum likelihood estimation by comparing these predictions with the true encodings. At the time of application, the true and predicted encodings are used to determine the probability of conformity, an interpretable and meaningful anomaly score. Furthermore, our approach has linear time complexity, scalability issues are prevented, and the method can easily be adjusted to a wide range of data types and intricate applications. We demonstrate the effectiveness and adaptability of our proposed method across synthetic time series experiments, image data, and real-world magnetic resonance spectroscopic imaging data.

Related papers

• Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection [88.34095233600719]
  FAPrompt is a novel framework designed to learn Fine-grained Abnormality Prompts for more accurate ZSAD. It substantially outperforms state-of-the-art methods by at least 3%-5% AUC/AP in both image- and pixel-level ZSAD tasks.
  arXiv  Detail & Related papers  (2024-10-14T08:41:31Z)
• USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time Series [6.055410677780381]
  We introduce a combination of data augmentation and soft contrastive learning, specifically designed to capture the multifaceted nature of state behaviors more accurately. This dual strategy significantly boosts the model's ability to distinguish between normal and abnormal states, leading to a marked improvement in fault detection performance across multiple datasets and settings.
  arXiv  Detail & Related papers  (2024-05-25T14:48:04Z)
• DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems [3.44012349879073]
  We present DeepHYDRA (Deep Hybrid DBSCAN/Reduction-Based Anomaly Detection) It combines DBSCAN and learning-based anomaly detection. It is shown to reliably detect different types of anomalies in both large and complex datasets.
  arXiv  Detail & Related papers  (2024-05-13T13:47:15Z)
• 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)
• Multi-scale Fusion Fault Diagnosis Method Based on Two-Dimensionaliztion Sequence in Complex Scenarios [0.0]
  Rolling bearings are critical components in rotating machinery, and their faults can cause severe damage. Early detection of abnormalities is crucial to prevent catastrophic accidents. Traditional and intelligent methods have been used to analyze time series data, but in real-life scenarios, sensor data is often noisy and cannot be accurately characterized in the time domain. This paper proposes an improved convolutional neural network method with a multi-scale feature fusion model and deep learning compression techniques for deployment in industrial scenarios.
  arXiv  Detail & Related papers  (2023-04-11T13:05:50Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Time-Series Anomaly Detection with Implicit Neural Representation [0.38073142980733]
  Implicit Neural Representation-based Anomaly Detection (INRAD) is proposed. We train a simple multi-layer perceptron that takes time as input and outputs corresponding values at that time. Then we utilize the representation error as an anomaly score for detecting anomalies.
  arXiv  Detail & Related papers  (2022-01-28T06:17:24Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals [10.866594993485226]
  We propose a novel deep learning-based anomaly detection algorithm called Deep Convolutional Autoencoding Memory network (CAE-M) We first build a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) Then, we construct a Memory Network consisting of linear (Autoregressive Model) and non-linear predictions (Bigressive LSTM with Attention) to capture temporal dependence from time-series data.
  arXiv  Detail & Related papers  (2021-07-27T06:48:20Z)
• 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)
• Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation [69.34035527763916]
  Change point detection (CPD) aims to locate abrupt property changes in time series data. Recent CPD methods demonstrated the potential of using deep learning techniques, but often lack the ability to identify more subtle changes in the autocorrelation statistics of the signal. We employ an autoencoder-based methodology with a novel loss function, through which the used autoencoders learn a partially time-invariant representation that is tailored for CPD.
  arXiv  Detail & Related papers  (2020-08-21T15:03:21Z)

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.