Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection

• URL: http://arxiv.org/abs/2401.03322v1
• Date: Sat, 6 Jan 2024 22:55:02 GMT
• Title: Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection
• Authors: Seyed Amirhossein Najafi, Mohammad Hassan Asemani, Peyman Setoodeh
• Abstract summary: This paper introduces a hybrid attention and autoencoder (AE) model for unsupervised online anomaly detection in time series. The autoencoder captures local structural patterns in short embeddings, while the attention model learns long-term features, facilitating parallel computing with positional encoding. It employs an attention-based mechanism, akin to the deep transformer model, with key architectural modifications for predicting the next time step window in the autoencoder's latent space.
• Score: 3.6049348666007934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a hybrid attention and autoencoder (AE) model for unsupervised online anomaly detection in time series. The autoencoder captures local structural patterns in short embeddings, while the attention model learns long-term features, facilitating parallel computing with positional encoding. Unique in its approach, our proposed hybrid model combines attention and autoencoder for the first time in time series anomaly detection. It employs an attention-based mechanism, akin to the deep transformer model, with key architectural modifications for predicting the next time step window in the autoencoder's latent space. The model utilizes a threshold from the validation dataset for anomaly detection and introduces an alternative method based on analyzing the first statistical moment of error, improving accuracy without dependence on a validation dataset. Evaluation on diverse real-world benchmark datasets and comparing with other well-established models, confirms the effectiveness of our proposed model in anomaly detection.

Related papers

• TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data [0.017476232824732776]
  We propose a temporal variational autoencoder (TeVAE) that can detect anomalies with minimal false positives when trained on unlabelled data. When properly configured, TeVAE flags anomalies only 6% of the time wrongly and detects 65% of anomalies present.
  arXiv  Detail & Related papers  (2024-07-09T13:32:33Z)
• An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone [3.1747517745997014]
  This paper presents an automated machine learning framework designed to assist hydrologists in detecting anomalies in time series data generated by sensors in a research watershed in the northeastern United States critical zone. The framework specifically focuses on identifying peak-pattern anomalies, which may arise from sensor malfunctions or natural phenomena.
  arXiv  Detail & Related papers  (2023-09-14T19:07:50Z)
• Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs [51.98400002538092]
  This paper aims to improve model-based anomaly detection in CPPS by combining the learned timed automaton with a formal knowledge graph about the system. Both the model and the detected anomalies are described in the knowledge graph in order to allow operators an easier interpretation of the model and the detected anomalies.
  arXiv  Detail & Related papers  (2023-08-25T15:25:57Z)
• LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection [12.596635603629725]
  We develop a unified model to detect anomalies from objects belonging to multiple classes when only normal data is accessible. We first explore the generative-based approach and investigate latent diffusion models for reconstruction. We introduce a feature editing strategy that modifies the input feature space of the diffusion model to further alleviate identity shortcuts''
  arXiv  Detail & Related papers  (2023-07-16T14:41:22Z)
• AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme [0.7216399430290167]
  Anomaly detection task for time series, especially for unlabeled data, has been a challenging problem. We address it by applying a suitable data degradation scheme to self-supervised model training. Inspired by the self-attention mechanism, we design a Transformer-based architecture to recognize the temporal context.
  arXiv  Detail & Related papers  (2023-05-08T05:42:24Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• ESAD: End-to-end Deep Semi-supervised Anomaly Detection [85.81138474858197]
  We propose a new objective function that measures the KL-divergence between normal and anomalous data. The proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-09T08:16:35Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)
• 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.