AnomalyKiTS: Anomaly Detection Toolkit for Time Series

Patel, Dhaval; Ganapavarapu, Giridhar; Jayaraman, Srideepika; Lin, Shuxin; Bhamidipaty, Anuradha; Kalagnanam, Jayant

doi:10.1609/aaai.v36i11.21730

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…It provides a range of algorithms, as well as an enrichment module to label identified anomalies. AnomalyKiTS offers four categories of model building capabilities, enabling users to select the best option for their needs [102].…”

Section: Ai Based Toolkits For Automated Anomaly Detectorsmentioning

confidence: 99%

Anomaly Detection in Time Series: Current Focus and Future Challenges

Arslan,

Javaid,

Danish Zaheer Awan

et al. 2024

Artificial Intelligence

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Anomaly detection in time series has become an increasingly vital task, with applications such as fraud detection and intrusion monitoring. Tackling this problem requires an array of approaches, including statistical analysis, machine learning, and deep learning. Various techniques have been proposed to cater to the complexity of this problem. However, there are still numerous challenges in the field concerning how best to process high-dimensional and complex data streams in real time. This chapter offers insight into the cutting-edge models for anomaly detection in time series. Several of the models are discussed and their advantages and disadvantages are explored. We also look at new areas of research that are being explored by researchers today as their current focuses and how those new models or techniques are being implemented in them as they try to solve unique problems posed by complex data, high-volume data streams, and a need for real-time processing. These research areas will provide concrete examples of the applications of discussed models. Lastly, we identify some of the current issues and suggest future directions for research concerning anomaly detection systems. We aim to provide readers with a comprehensive picture of what is already out there so they can better understand the space – preparing them for further development within this growing field.

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Section: Ai Based Toolkits For Automated Anomaly Detectorsmentioning

confidence: 99%

Anomaly Detection in Time Series: Current Focus and Future Challenges

Arslan,

Javaid,

Danish Zaheer Awan

et al. 2024

Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…The key novelty of ANOVIZ resides in its unique focus on supporting users with visual inspection ability and a more informed explanation of anomalies in multivariate time series. Unlike previous work focusing on finding an optimal anomaly detector based on quantitative metrics, rarely with a simple visualization (Patel et al 2022;Lai et al 2021;Khelifati et al 2021;Li et al 2020;Eichmann et al 2019) or locating potential root causes without visualization of relevant anomalies (Georgieva et al 2022), ANOVIZ allows users to quickly specify the potential culprits and appropriately handle them based on the provided relevant insights. Furthermore, in addition to the query mode used by most existing work, our work also delivers a stream mode, enabling users to continuously analyze the results in near real-time.…”

Section: Noveltymentioning

confidence: 99%

AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series

Trirat

Nam

Kim

et al. 2023

AAAI

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This paper presents AnoViz, a novel visualization tool of anomalies in multivariate time series, to support domain experts and data scientists in understanding anomalous instances in their systems. AnoViz provides an overall summary of time series as well as detailed visualizations of relevant detected anomalies in both query and stream modes, rendering near real-time visual analysis available. Here, we show that AnoViz streamlines the process of finding a potential cause of an anomaly with a deeper analysis of anomalous instances, giving explainability to any anomaly detector.

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“…Machine learning has demonstrated immense potential across various industries, and the demand for more sophisticated, cutting-edge AI technology is growing in numerous applications (Patel et al 2023). Currently, machine learning heavily relies on feature extraction algorithms to extract valuable insights from large-scale datasets.…”

Section: Introductionmentioning

confidence: 99%

A Learnable Discrete-Prior Fusion Autoencoder with Contrastive Learning for Tabular Data Synthesis

Zhang,

Lou,

et al. 2024

AAAI

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The actual collection of tabular data for sharing involves confidentiality and privacy constraints, leaving the potential risks of machine learning for interventional data analysis unsafely averted. Synthetic data has emerged recently as a privacy-protecting solution to address this challenge. However, existing approaches regard discrete and continuous modal features as separate entities, thus falling short in properly capturing their inherent correlations. In this paper, we propose a novel contrastive learning guided Gaussian Transformer autoencoder, termed GTCoder, to synthesize photo-realistic multimodal tabular data for scientific research. Our approach introduces a transformer-based fusion module that seamlessly integrates multimodal features, permitting for mining more informative latent representations. The attention within the fusion module directs the integrated output features to focus on critical components that facilitate the task of generating latent embeddings. Moreover, we formulate a contrastive learning strategy to implicitly constrain the embeddings from discrete features in the latent feature space by encouraging the similar discrete feature distributions closer while pushing the dissimilar further away, in order to better enhance the representation of the latent embedding. Experimental results indicate that GTCoder is effective to generate photo-realistic synthetic data, with interactive interpretation of latent embedding, and performs favorably against some baselines on most real-world and simulated datasets.

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AnomalyKiTS: Anomaly Detection Toolkit for Time Series

Cited by 11 publications

References 12 publications

Anomaly Detection in Time Series: Current Focus and Future Challenges

Anomaly Detection in Time Series: Current Focus and Future Challenges

AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series

A Learnable Discrete-Prior Fusion Autoencoder with Contrastive Learning for Tabular Data Synthesis

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