Model-free detection of unique events in time series
Abstract Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown. In this paper, we introduce a new anomaly concept called “unicorn” or unique event and present a new, model-free, unsupervise...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-01-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-03526-y |
| _version_ | 1798034721874116608 |
|---|---|
| author | Zsigmond Benkő Tamás Bábel Zoltán Somogyvári |
| author_facet | Zsigmond Benkő Tamás Bábel Zoltán Somogyvári |
| author_sort | Zsigmond Benkő |
| collection | DOAJ |
| description | Abstract Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown. In this paper, we introduce a new anomaly concept called “unicorn” or unique event and present a new, model-free, unsupervised detection algorithm to detect unicorns. The key component of the new algorithm is the Temporal Outlier Factor (TOF) to measure the uniqueness of events in continuous data sets from dynamic systems. The concept of unique events differs significantly from traditional outliers in many aspects: while repetitive outliers are no longer unique events, a unique event is not necessarily an outlier; it does not necessarily fall out from the distribution of normal activity. The performance of our algorithm was examined in recognizing unique events on different types of simulated data sets with anomalies and it was compared with the Local Outlier Factor (LOF) and discord discovery algorithms. TOF had superior performance compared to LOF and discord detection algorithms even in recognizing traditional outliers and it also detected unique events that those did not. The benefits of the unicorn concept and the new detection method were illustrated by example data sets from very different scientific fields. Our algorithm successfully retrieved unique events in those cases where they were already known such as the gravitational waves of a binary black hole merger on LIGO detector data and the signs of respiratory failure on ECG data series. Furthermore, unique events were found on the LIBOR data set of the last 30 years. |
| first_indexed | 2024-04-11T20:48:12Z |
| format | Article |
| id | doaj.art-5b7d2136b4614ad5a28b2c9c6f93edfb |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T20:48:12Z |
| publishDate | 2022-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-5b7d2136b4614ad5a28b2c9c6f93edfb2022-12-22T04:03:57ZengNature PortfolioScientific Reports2045-23222022-01-0112111710.1038/s41598-021-03526-yModel-free detection of unique events in time seriesZsigmond Benkő0Tamás Bábel1Zoltán Somogyvári2Department of Computational Sciences, Wigner Research Centre for PhysicsDepartment of Computational Sciences, Wigner Research Centre for PhysicsDepartment of Computational Sciences, Wigner Research Centre for PhysicsAbstract Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown. In this paper, we introduce a new anomaly concept called “unicorn” or unique event and present a new, model-free, unsupervised detection algorithm to detect unicorns. The key component of the new algorithm is the Temporal Outlier Factor (TOF) to measure the uniqueness of events in continuous data sets from dynamic systems. The concept of unique events differs significantly from traditional outliers in many aspects: while repetitive outliers are no longer unique events, a unique event is not necessarily an outlier; it does not necessarily fall out from the distribution of normal activity. The performance of our algorithm was examined in recognizing unique events on different types of simulated data sets with anomalies and it was compared with the Local Outlier Factor (LOF) and discord discovery algorithms. TOF had superior performance compared to LOF and discord detection algorithms even in recognizing traditional outliers and it also detected unique events that those did not. The benefits of the unicorn concept and the new detection method were illustrated by example data sets from very different scientific fields. Our algorithm successfully retrieved unique events in those cases where they were already known such as the gravitational waves of a binary black hole merger on LIGO detector data and the signs of respiratory failure on ECG data series. Furthermore, unique events were found on the LIBOR data set of the last 30 years.https://doi.org/10.1038/s41598-021-03526-y |
| spellingShingle | Zsigmond Benkő Tamás Bábel Zoltán Somogyvári Model-free detection of unique events in time series Scientific Reports |
| title | Model-free detection of unique events in time series |
| title_full | Model-free detection of unique events in time series |
| title_fullStr | Model-free detection of unique events in time series |
| title_full_unstemmed | Model-free detection of unique events in time series |
| title_short | Model-free detection of unique events in time series |
| title_sort | model free detection of unique events in time series |
| url | https://doi.org/10.1038/s41598-021-03526-y |
| work_keys_str_mv | AT zsigmondbenko modelfreedetectionofuniqueeventsintimeseries AT tamasbabel modelfreedetectionofuniqueeventsintimeseries AT zoltansomogyvari modelfreedetectionofuniqueeventsintimeseries |