2023
1.
Shi, Wen; Azzopardi, George; Karastoyanova, Dimka; Huang, Yongming
Bidirectional Piecewise Linear Representation of Time Series with Application to Collective Anomaly Detection Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: anomaly detection, time-series
@article{Shi2023,
title = {Bidirectional Piecewise Linear Representation of Time Series with Application to Collective Anomaly Detection},
author = {Shi, Wen and Azzopardi, George and Karastoyanova, Dimka and Huang, Yongming},
doi = {https://doi.org/10.1016/j.aei.2023.102155},
year = {2023},
date = {2023-09-06},
urldate = {2023-09-06},
journal = {Advanced Engineering Informatics},
volume = {58},
number = {102155},
abstract = {Directly mining high-dimensional time series presents several challenges, such as time and space costs. This study proposes a new approach for representing time series data and evaluates its effectiveness in detecting collective anomalies. The proposed method, called bidirectional piecewise linear representation (BPLR), represents the original time series using a set of linear fitting functions, which allows for dimensionality reduction while maintaining its dynamic characteristics. Similarity measurement is then performed using the piecewise integration (PI) approach, which achieves good detection performance with low computational overhead. Experimental results on synthetic and real-world data sets confirm the effectiveness and advantages of the proposed approach. The ability of the proposed method to capture more dynamic details of time series leads to consistently superior performance compared to other existing methods.},
keywords = {anomaly detection, time-series},
pubstate = {published},
tppubtype = {article}
}
Directly mining high-dimensional time series presents several challenges, such as time and space costs. This study proposes a new approach for representing time series data and evaluates its effectiveness in detecting collective anomalies. The proposed method, called bidirectional piecewise linear representation (BPLR), represents the original time series using a set of linear fitting functions, which allows for dimensionality reduction while maintaining its dynamic characteristics. Similarity measurement is then performed using the piecewise integration (PI) approach, which achieves good detection performance with low computational overhead. Experimental results on synthetic and real-world data sets confirm the effectiveness and advantages of the proposed approach. The ability of the proposed method to capture more dynamic details of time series leads to consistently superior performance compared to other existing methods.
2019
2.
Neocleous, Andreas; Azzopardi, George; Dee, Michael
Identification of possible Δ14C anomalies since 14 ka BP: A computational intelligence approach Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: anomaly detection, brain-inspired, predictive analysis, time-series, trainable filters
@article{neocleous2019identification,
title = {Identification of possible Δ14C anomalies since 14 ka BP: A computational intelligence approach},
author = {Andreas Neocleous and George Azzopardi and Michael Dee},
doi = {10.1016/j.scitotenv.2019.01.251},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {Science of The Total Environment},
volume = {663},
pages = {162--169},
publisher = {Elsevier},
abstract = {Rapid increments in the concentration of the radiocarbon in the atmosphere (Δ14C) have been identified in the years 774-775 CE and 993-994 CE (Miyake events) using annual measurements on known-age tree-rings. The level of cosmic radiation implied by such increases could cause the failure of satellite telecommunication systems, and thus, there is a need to model and predict them. In this work, we investigated several intelligent computational methods to identify similar events in the past. We apply state-of-the-art pattern matching techniques as well as feature representation, a procedure that typically is used in machine learning and classification. To validate our findings, we used as ground truth the two confirmed Miyake events, and several other dates that have been proposed in the literature. We show that some of the methods used in this study successfully identify most of the ground truth events (~1% false positive rate at 75% true positive rate). Our results show that computational methods can be used to identify comparable patterns of interest and hence potentially uncover sudden increments of Δ14C in the past.},
keywords = {anomaly detection, brain-inspired, predictive analysis, time-series, trainable filters},
pubstate = {published},
tppubtype = {article}
}
Rapid increments in the concentration of the radiocarbon in the atmosphere (Δ14C) have been identified in the years 774-775 CE and 993-994 CE (Miyake events) using annual measurements on known-age tree-rings. The level of cosmic radiation implied by such increases could cause the failure of satellite telecommunication systems, and thus, there is a need to model and predict them. In this work, we investigated several intelligent computational methods to identify similar events in the past. We apply state-of-the-art pattern matching techniques as well as feature representation, a procedure that typically is used in machine learning and classification. To validate our findings, we used as ground truth the two confirmed Miyake events, and several other dates that have been proposed in the literature. We show that some of the methods used in this study successfully identify most of the ground truth events (~1% false positive rate at 75% true positive rate). Our results show that computational methods can be used to identify comparable patterns of interest and hence potentially uncover sudden increments of Δ14C in the past.
3.
Neocleous, Andreas; Azzopardi, George; Kuitems, Margot; Scifo, Andrea; Dee, Michael
Trainable Filters for the Identification of Anomalies in Cosmogenic Isotope Data Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: anomaly detection, brain-inspired, predictive analysis, time-series, trainable filters
@article{neocleous2019trainable,
title = {Trainable Filters for the Identification of Anomalies in Cosmogenic Isotope Data},
author = {Andreas Neocleous and George Azzopardi and Margot Kuitems and Andrea Scifo and Michael Dee},
doi = {10.1109/ACCESS.2019.2900123},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {IEEE Access},
volume = {7},
pages = {24585--24592},
publisher = {IEEE},
abstract = {Extreme bursts of radiation from space result in rapid increases in the concentration of radiocarbon in the atmosphere. Such rises, known as Miyake Events, can be detected through the measurement of radiocarbon in dendrochronological archives. The identification of Miyake Events is important because radiation impacts of this magnitude pose an existential threat to satellite communications and aeronautical avionics and may even be detrimental to human health. However, at present, radiocarbon measurements on tree-ring archives are generally only available at decadal resolution, which smooths out the effect of a possible radiation burst. The Miyake Events discovered so far, in tree-rings from the years 3372-3371 BCE, 774-775 CE, and 993-994 CE, have essentially been found by chance, but there may be more. In this paper, we use signal processing techniques, in particular COSFIRE, to train filters with data on annual changes in radiocarbon (Δ 14 C) around those dates. Then, we evaluate the trained filters and attempt to detect similar Miyake Events in the past. The method that we propose is promising, since it identifies the known Miyake Events at a relatively low false positive rate. Using the findings of this paper, we propose a list of 26 calendar years that our system persistently indicates are Miyake Event-like. We are currently examining a short-list of five of the newly identified dates and intend to perform single-year radiocarbon measurements over them. Signal processing techniques, such as COSFIRE filters, can be used as guidance tools since they are able to identify similar patterns of interest, even if they vary in time or in amplitude.},
keywords = {anomaly detection, brain-inspired, predictive analysis, time-series, trainable filters},
pubstate = {published},
tppubtype = {article}
}
Extreme bursts of radiation from space result in rapid increases in the concentration of radiocarbon in the atmosphere. Such rises, known as Miyake Events, can be detected through the measurement of radiocarbon in dendrochronological archives. The identification of Miyake Events is important because radiation impacts of this magnitude pose an existential threat to satellite communications and aeronautical avionics and may even be detrimental to human health. However, at present, radiocarbon measurements on tree-ring archives are generally only available at decadal resolution, which smooths out the effect of a possible radiation burst. The Miyake Events discovered so far, in tree-rings from the years 3372-3371 BCE, 774-775 CE, and 993-994 CE, have essentially been found by chance, but there may be more. In this paper, we use signal processing techniques, in particular COSFIRE, to train filters with data on annual changes in radiocarbon (Δ 14 C) around those dates. Then, we evaluate the trained filters and attempt to detect similar Miyake Events in the past. The method that we propose is promising, since it identifies the known Miyake Events at a relatively low false positive rate. Using the findings of this paper, we propose a list of 26 calendar years that our system persistently indicates are Miyake Event-like. We are currently examining a short-list of five of the newly identified dates and intend to perform single-year radiocarbon measurements over them. Signal processing techniques, such as COSFIRE filters, can be used as guidance tools since they are able to identify similar patterns of interest, even if they vary in time or in amplitude.