Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector
- URL: http://arxiv.org/abs/2501.15053v1
- Date: Sat, 25 Jan 2025 03:26:22 GMT
- Title: Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector
- Authors: Aafan Ahmad Toor, Jia-Chun Lin, Ernst Gunnar Gran,
- Abstract summary: This study explores the impact of automatically tuned hyperparamteres on Unsupervised Online Contextual Anomaly Detection (UoCAD) approach by proposing UoCAD with Optimised Hyperparamnters (UoCAD-OH)
Experiments involve evaluating the proposed framework on two smart home air quality datasets containing contextual anomalies.
- Score: 0.09831489366502298
- License:
- Abstract: The exponential growth in the usage of Internet of Things in daily life has caused immense increase in the generation of time series data. Smart homes is one such domain where bulk of data is being generated and anomaly detection is one of the many challenges addressed by researchers in recent years. Contextual anomaly is a kind of anomaly that may show deviation from the normal pattern like point or sequence anomalies, but it also requires prior knowledge about the data domain and the actions that caused the deviation. Recent studies based on Recurrent Neural Networks (RNN) have demonstrated strong performance in anomaly detection. This study explores the impact of automatically tuned hyperparamteres on Unsupervised Online Contextual Anomaly Detection (UoCAD) approach by proposing UoCAD with Optimised Hyperparamnters (UoCAD-OH). UoCAD-OH conducts hyperparameter optimisation on Bi-LSTM model in an offline phase and uses the fine-tuned hyperparameters to detect anomalies during the online phase. The experiments involve evaluating the proposed framework on two smart home air quality datasets containing contextual anomalies. The evaluation metrics used are Precision, Recall, and F1 score.
Related papers
- 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) - Unraveling the "Anomaly" in Time Series Anomaly Detection: A
Self-supervised Tri-domain Solution [89.16750999704969]
Anomaly labels hinder traditional supervised models in time series anomaly detection.
Various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue.
We propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD)
arXiv Detail & Related papers (2023-11-19T05:37:18Z) - Are we certain it's anomalous? [57.729669157989235]
Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations.
Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD)
HypAD learns self-supervisedly to reconstruct the input signal.
arXiv Detail & Related papers (2022-11-16T21:31:39Z) - DEGAN: Time Series Anomaly Detection using Generative Adversarial
Network Discriminators and Density Estimation [0.0]
We have proposed an unsupervised Generative Adversarial Network (GAN)-based anomaly detection framework, DEGAN.
It relies solely on normal time series data as input to train a well-configured discriminator (D) into a standalone anomaly predictor.
arXiv Detail & Related papers (2022-10-05T04:32:12Z) - An Outlier Exposure Approach to Improve Visual Anomaly Detection
Performance for Mobile Robots [76.36017224414523]
We consider the problem of building visual anomaly detection systems for mobile robots.
Standard anomaly detection models are trained using large datasets composed only of non-anomalous data.
We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
arXiv Detail & Related papers (2022-09-20T15:18:13Z) - LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time
Series Data [6.642599588462097]
Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being.
Traditional statistics and machine learning-based approaches in anomaly detection in the IAQ area could not detect anomalies involving the observation of correlations across several data points.
We propose a hybrid deep learning model that combines LSTM with Autoencoder for anomaly detection tasks in IAQ to address this issue.
arXiv Detail & Related papers (2022-04-14T01:57:46Z) - 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) - 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)
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.