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A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-47887-6_53openalex-snapshot
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
inferredcites →https://doi.org/10.1007/s10618-023-00988-8openalex-snapshot
Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series
inferredcites →https://doi.org/10.1016/j.actaastro.2022.06.026openalex-snapshot
Evaluating algorithms for anomaly detection in satellite telemetry data
inferredcites →https://doi.org/10.1016/j.neucom.2021.03.062openalex-snapshot
A joint model for IT operation series prediction and anomaly detection
inferredcites →https://doi.org/10.1016/j.oceaneng.2019.106129openalex-snapshot
Study on wavelet neural network based anomaly detection in ocean observing data series
inferredcites →https://doi.org/10.1057/9781137291264_6openalex-snapshot
Box and Jenkins: Time Series Analysis, Forecasting and Control
inferredcites →https://doi.org/10.1093/bib/bbr008openalex-snapshot
Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)
inferredcites →https://doi.org/10.1109/bigdata50022.2020.9378139openalex-snapshot
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
inferredcites →https://doi.org/10.1109/icdm50108.2020.00093openalex-snapshot
Multivariate Time-Series Anomaly Detection via Graph Attention Network
inferredcites →https://doi.org/10.1109/icmla.2015.141openalex-snapshot
Evaluating Real-Time Anomaly Detection Algorithms -- The Numenta Anomaly Benchmark
inferredcites →https://doi.org/10.1109/jiot.2019.2958185openalex-snapshot
Anomaly Detection for IoT Time-Series Data: A Survey
inferredcites →https://doi.org/10.1109/jiot.2021.3100509openalex-snapshot
Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT
inferredcites →https://doi.org/10.1109/jiot.2023.3293860openalex-snapshot
An Interpretable Multivariate Time-Series Anomaly Detection Method in Cyber–Physical Systems Based on Adaptive Mask
inferredcites →https://doi.org/10.1109/jiot.2023.3303946openalex-snapshot
Multiview Graph Contrastive Learning for Multivariate Time-Series Anomaly Detection in IoT
inferredcites →https://doi.org/10.1109/jiot.2024.3446570openalex-snapshot
Deep Koopman Predictors for Anomaly Detection of Complex IoT Systems With Time Series Data
inferredcites →https://doi.org/10.1109/lra.2018.2801475openalex-snapshot
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder
inferredcites →https://doi.org/10.1109/ssci47803.2020.9308512openalex-snapshot
TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks
inferredcites →https://doi.org/10.1109/tkde.2021.3130234openalex-snapshot
DCT-GAN: Dilated Convolutional Transformer-Based GAN for Time Series Anomaly Detection
inferredcites →https://doi.org/10.1109/tkde.2021.3140058openalex-snapshot
Time Series Anomaly Detection With Adversarial Reconstruction Networks
inferredcites →https://doi.org/10.1109/tkde.2024.3393996openalex-snapshot
Calibrated One-Class Classification for Unsupervised Time Series Anomaly Detection
inferredcites →https://doi.org/10.1109/tnnls.2019.2935975openalex-snapshot
Unsupervised Anomaly Detection With LSTM Neural Networks
inferredcites →https://doi.org/10.1109/tnnls.2021.3105827openalex-snapshot
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series
inferredcites →https://doi.org/10.1109/tnnls.2022.3162949openalex-snapshot
Unsupervised Multimodal Anomaly Detection With Missing Sources for Liquid Rocket Engine
inferredcites →https://doi.org/10.1111/j.1466-8238.2007.00358.xopenalex-snapshot
AUC: a misleading measure of the performance of predictive distribution models
inferredcites →https://doi.org/10.1145/1143844.1143874openalex-snapshot
The relationship between Precision-Recall and ROC curves
inferredcites →https://doi.org/10.1145/3219819.3219845openalex-snapshot
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
inferredcites →https://doi.org/10.1145/3442381.3450013openalex-snapshot
SDFVAE: Static and Dynamic Factorized VAE for Anomaly Detection of Multivariate CDN KPIs
inferredcites →https://doi.org/10.1145/3442381.3450023openalex-snapshot
MStream: Fast Anomaly Detection in Multi-Aspect Streams
inferredcites →https://doi.org/10.1145/3477314.3507024openalex-snapshot
"Do you know existing accuracy metrics overrate time-series anomaly detections?"
inferredcites →https://doi.org/10.1145/3485447.3511984openalex-snapshot
A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online Systems
inferredcites →https://doi.org/10.1145/3637528.3671971openalex-snapshot
PATE: Proximity-Aware Time Series Anomaly Evaluation
inferredcites →https://doi.org/10.1198/016214501753168136openalex-snapshot
A Proposed Design and Analysis for Comparing Digital and Analog Mammography
inferredcites →https://doi.org/10.14778/3425879.3425885openalex-snapshot
Real-time distance-based outlier detection in data streams
inferredcites →https://doi.org/10.1609/aaai.v35i5.16523openalex-snapshot
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
inferredcites →https://doi.org/10.1609/aaai.v36i7.20680openalex-snapshot
Towards a Rigorous Evaluation of Time-Series Anomaly Detection
inferredcites →https://doi.org/10.1609/aaai.v36i8.20881openalex-snapshot
TS2Vec: Towards Universal Representation of Time Series
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1016/j.mlwa.2026.100986openalex-snapshot
A segmentation-based episodic representation framework for cross-design comparison in multi-mode industrial time series
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