# Uncertainty-Aware Time Series Anomaly Detection
## 3 Proposed Approach
### Thresholding Mechanism
In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, AK, USA, 4-8 August 2021. * (43) [PERSON]; [PERSON]; [PERSON]/MGAB: The Mackey-Glass Anomaly Benchmark; Version v1. 0.1.; Zenodo: Geneva, Switzerland, 2020; Volume 10. * (44) [PERSON]; [PERSON] _MIT-BIH Arrhythmia Database Directory_; MITBIH Database Distribution, Harvard--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology: Cambridge, MA, USA, 1992. Available online: [[http://www.physionet.org/physiobank/database/html/mitdbdir/mitdbdir.htm](http://www.physionet.org/physiobank/database/html/mitdbdir/mitdbdir.htm)]([http://www.physionet.org/physiobank/database/html/mitdbdir/mitdbdir.htm](http://www.physionet.org/physiobank/database/html/mitdbdir/mitdbdir.htm)) (accessed on 23 July 2008). * (45) [PERSON]; [PERSON]; [PERSON]; [PERSON] Unsupervised real-time anomaly detection for streaming data. _Neurocomputing_**2017**, _262_, 134-147. [CrossRef]
* (46) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Detecting anomalies in spacecraft telemetry using evolutionary thresholding and LSTMs. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, Lille, France, 10-14 July 2021; pp. 143-144. * (47) [PERSON]; [PERSON] Accurate occupancy detection of an office room from light, temperature, humidity and CO\({}_{2}\) measurements using statistical learning models. _Energy Build._**2016**, _112_, 28-39. [CrossRef]
* (48) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; et al. Collecting complex activity datasets in highly rich networked sensor environments. In Proceedings of the 2010 Seventh International Conference on Networked Sensing Systems (INSS), Kassel, Germany, 15-18 June 2010; pp. 233-240. * (49) [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Online anomaly detection for sensor systems: A simple and efficient approach. _Perform. Eval._**2010**, _67_, 1059-1075. [CrossRef]
* (50) [PERSON]; [PERSON]; [PERSON] _Improved Detection and Classification of Arrhythmias in Noise-Corrupted Electrocardiograms Using Contextual Information_; IEEE: Piscataway, NJ, USA, 1990. * (51) [PERSON]; [PERSON]; [PERSON] _S5-A Labeled Anomaly Detection Dataset, Version 1.0 (16M)_; Trinity College: Dublin, Ireland, 2015. * (52) [PERSON]; [PERSON]; [PERSON] Isolation forest. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, 15-19 December 2008; pp. 413-422. * (53) [PERSON]; [PERSON]; [PERSON]; [PERSON] LOF: Identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX, USA, 15-18 May 2000; pp. 93-104. * (54) [PERSON]; [PERSON] Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. _KI-2012 Poster Demo Track_**2012**, \(1\), 59-63. * (55) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Unsupervised and scalable subsequence anomaly detection in large data series. _VLDB J._**2021**, _30_, 909-931. [CrossRef]
* (56) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Support vector method for novelty detection. _Adv. Neural Inf. Process. Syst._**1999**, _12_, 588. * (57) [PERSON]; [PERSON] _An Introduction to Outlier Analysis_; Springer: Berlin/Heidelberg, Germany, 2017. * (58) [PERSON]; [PERSON]; [PERSON]; [PERSON] DeepAnT: A deep learning approach for unsupervised anomaly detection in time series. _IEEE Access_**2018**, \(7\), 1991-2005. [CrossRef]

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.