# Uncertainty-Aware Time Series Anomaly Detection
## 3 Proposed Approach
### Thresholding Mechanism
(2018)_ [PERSON]; [PERSON]; [PERSON] A novel anomaly detection algorithm for sensor data under uncertainty. _Soft Comput._**2018**, _22_, 1623-1639. [CrossRef]
* _[PERSON] (2024)_ [PERSON]; [PERSON] [PERSON] Uncertainty Quantification in Anomaly Detection with Cross-Conformal \(p\)-Values. _arXiv_**2024**, arXiv:2402.16388. * _[PERSON] et al. (2015)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Weight uncertainty in neural network. In Proceedings of the International Conference on Machine Learning, PMLR, Lille, France, 6-11 July 2015; pp. 1613-1622. * _[PERSON] et al. (2009)_ [PERSON]; [PERSON] _Bayesian Theory_; John Wiley & Sons: Hoboken, NJ, USA, 2009; Volume 405. Available online: [[https://statisticisupportnresearch.wordpress.com/wp-content/uploads/2019/03/josc3a9-m-.-bernardo-adrian-f-.-m.-smith-bayesian-theory-wiley-1994.pdf](https://statisticisupportnresearch.wordpress.com/wp-content/uploads/2019/03/josc3a9-m-.-bernardo-adrian-f-.-m.-smith-bayesian-theory-wiley-1994.pdf)]([https://statisticisupportnresearch.wordpress.com/wp-content/uploads/2019/03/josc3a9-m-.-bernardo-adrian-f-.-m.-smith-bayesian-theory-wiley-1994.pdf](https://statisticisupportnresearch.wordpress.com/wp-content/uploads/2019/03/josc3a9-m-.-bernardo-adrian-f-.-m.-smith-bayesian-theory-wiley-1994.pdf)) (accessed on 1 September 2024). * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON] Use of uncertainty with autoencoder neural networks for anomaly detection. In Proceedings of the 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Sardinia, Italy, 3-5 July 2019; pp. 32-35. * _[PERSON] (2019)_ [PERSON]; [PERSON] [PERSON] Bayesian variational autoencoders for unsupervised out-of-distribution detection. _arXiv_**2019**, arXiv:1912.05651. * _[PERSON] (2022)_ [PERSON]; [PERSON] [PERSON] Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression. _arXiv_**2022**, arXiv:2211.06233. * _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Imbalanced satellite telemetry data anomaly detection model based on Bayesian LSTM. _Acta Astronaut._**2021**, _180_, 232-242. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON] Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection. _Expert Syst. Appl._**2022**, _209_, 118196. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON] TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms. _Proc. VLDB Endow._**2022**, _15_, 3678-3681. [CrossRef]
* _[PERSON] et al. (2014)_ [PERSON]; [PERSON] Top-k outlier detection from uncertain data. _Int. J. Autom. Comput._**2014**, _11_, 128-142. [CrossRef]
* _[PERSON] et al. (2009)_ [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Wearable assistant for Parkinson's disease patients with the freezing of gait symptom. _IEEE Trans. Inf. Technol. Biomed._**2009**, _14_, 436-446. [CrossRef] [PubMed]* (38) [PERSON]; [PERSON] [PERSON]; [PERSON] Adaptive event detection with time-varying poisson processes. In Proceedings of the 12 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Long Beach, CA, USA, 6-10 August 2006; pp. 207-216. * (39) [PERSON]; [PERSON] The impact of the MIT-BIH arrhythmia database. _IEEE Eng. Med. Biol. Mag._**2001**, _20_, 45-50. [CrossRef] [PubMed]
* (40) [PERSON] [PERSON]; [PERSON] [PERSON] Anomaly detection and localization for cyber-physical production systems with self-organizing maps. In _IMPROVE-Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency: Intelligent Methods for the Factory of the Future_; Springer: Berlin/Heidelberg, Germany, 2018; pp. 55-71. * (41) [PERSON]; [PERSON]; [PERSON] [PERSON] Multivariate industrial time series with cyber-attack simulation: Fault detection using an lstm-based predictive data model. _arXiv_**2016**, arXiv:1612.06676. * (42) [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-dataset time-series anomaly detection competition.