Standard
Robust Functional Regression for Outlier Detection. /
Hullait, H.; Leslie, D.S.; Pavlidis, N.G. et al.
Advanced Analytics and Learning on Temporal Data: 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers. ed. / Vincent Lemaire; Simon Malinowski; Anthony Bagnall; Alexis Bondu; Thomas Guyet; Romain Tavenard. Cham: Springer, 2020. p. 3-13 (Lecture Notes in Computer Science; Vol. 11986).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Hullait, H, Leslie, DS, Pavlidis, NG & King, S 2020,
Robust Functional Regression for Outlier Detection. in V Lemaire, S Malinowski, A Bagnall, A Bondu, T Guyet & R Tavenard (eds),
Advanced Analytics and Learning on Temporal Data: 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers. Lecture Notes in Computer Science, vol. 11986, Springer, Cham, pp. 3-13.
https://doi.org/10.1007/978-3-030-39098-3_1
APA
Hullait, H., Leslie, D. S., Pavlidis, N. G., & King, S. (2020).
Robust Functional Regression for Outlier Detection. In V. Lemaire, S. Malinowski, A. Bagnall, A. Bondu, T. Guyet, & R. Tavenard (Eds.),
Advanced Analytics and Learning on Temporal Data: 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers (pp. 3-13). (Lecture Notes in Computer Science; Vol. 11986). Springer.
https://doi.org/10.1007/978-3-030-39098-3_1
Vancouver
Hullait H, Leslie DS, Pavlidis NG, King S.
Robust Functional Regression for Outlier Detection. In Lemaire V, Malinowski S, Bagnall A, Bondu A, Guyet T, Tavenard R, editors, Advanced Analytics and Learning on Temporal Data: 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers. Cham: Springer. 2020. p. 3-13. (Lecture Notes in Computer Science). Epub 2019 Sept 20. doi: 10.1007/978-3-030-39098-3_1
Author
Hullait, H. ; Leslie, D.S. ; Pavlidis, N.G. et al. /
Robust Functional Regression for Outlier Detection. Advanced Analytics and Learning on Temporal Data: 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers. editor / Vincent Lemaire ; Simon Malinowski ; Anthony Bagnall ; Alexis Bondu ; Thomas Guyet ; Romain Tavenard. Cham : Springer, 2020. pp. 3-13 (Lecture Notes in Computer Science).
Bibtex
@inproceedings{4a31825d111b4644af504c54da57ad6e,
title = "Robust Functional Regression for Outlier Detection",
abstract = "In this paper we propose an outlier detection algorithm for temperature sensor data from jet engine tests. Effective identification of outliers would enable engine problems to be examined and resolved efficiently. Outlier detection in this data is challenging because a human controller determines the speed of the engine during each manoeuvre. This introduces variability which can mask abnormal behaviour in the engine response. We therefore suggest modelling the dependency between speed and temperature in the process of identifying abnormalities. The engine temperature has a delayed response with respect to the engine speed, which we will model using robust functional regression. We then apply functional depth with respect to the residuals to rank the samples and identify the outliers. The effectiveness of the outlier detection algorithm is shown in a simulation study. The algorithm is also applied to real engine data, and identifies samples that warrant further investigation. ",
keywords = "Outlier detection, Robust functional data analysis, Robust model selection, Advanced Analytics, Anomaly detection, Data handling, Engines, Signal detection, Abnormal behaviours, Engine temperatures, Functional data analysis, Functional regression, Human controllers, Outlier detection algorithm, Robust model selections, Simulation studies, Statistics",
author = "H. Hullait and D.S. Leslie and N.G. Pavlidis and S. King",
year = "2020",
month = jan,
day = "23",
doi = "10.1007/978-3-030-39098-3_1",
language = "English",
isbn = "9783030390976 ",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "3--13",
editor = "Vincent Lemaire and Simon Malinowski and Anthony Bagnall and Alexis Bondu and Thomas Guyet and Romain Tavenard",
booktitle = "Advanced Analytics and Learning on Temporal Data",
}
RIS
TY - GEN
T1 - Robust Functional Regression for Outlier Detection
AU - Hullait, H.
AU - Leslie, D.S.
AU - Pavlidis, N.G.
AU - King, S.
PY - 2020/1/23
Y1 - 2020/1/23
N2 - In this paper we propose an outlier detection algorithm for temperature sensor data from jet engine tests. Effective identification of outliers would enable engine problems to be examined and resolved efficiently. Outlier detection in this data is challenging because a human controller determines the speed of the engine during each manoeuvre. This introduces variability which can mask abnormal behaviour in the engine response. We therefore suggest modelling the dependency between speed and temperature in the process of identifying abnormalities. The engine temperature has a delayed response with respect to the engine speed, which we will model using robust functional regression. We then apply functional depth with respect to the residuals to rank the samples and identify the outliers. The effectiveness of the outlier detection algorithm is shown in a simulation study. The algorithm is also applied to real engine data, and identifies samples that warrant further investigation.
AB - In this paper we propose an outlier detection algorithm for temperature sensor data from jet engine tests. Effective identification of outliers would enable engine problems to be examined and resolved efficiently. Outlier detection in this data is challenging because a human controller determines the speed of the engine during each manoeuvre. This introduces variability which can mask abnormal behaviour in the engine response. We therefore suggest modelling the dependency between speed and temperature in the process of identifying abnormalities. The engine temperature has a delayed response with respect to the engine speed, which we will model using robust functional regression. We then apply functional depth with respect to the residuals to rank the samples and identify the outliers. The effectiveness of the outlier detection algorithm is shown in a simulation study. The algorithm is also applied to real engine data, and identifies samples that warrant further investigation.
KW - Outlier detection
KW - Robust functional data analysis
KW - Robust model selection
KW - Advanced Analytics
KW - Anomaly detection
KW - Data handling
KW - Engines
KW - Signal detection
KW - Abnormal behaviours
KW - Engine temperatures
KW - Functional data analysis
KW - Functional regression
KW - Human controllers
KW - Outlier detection algorithm
KW - Robust model selections
KW - Simulation studies
KW - Statistics
U2 - 10.1007/978-3-030-39098-3_1
DO - 10.1007/978-3-030-39098-3_1
M3 - Conference contribution/Paper
SN - 9783030390976
T3 - Lecture Notes in Computer Science
SP - 3
EP - 13
BT - Advanced Analytics and Learning on Temporal Data
A2 - Lemaire, Vincent
A2 - Malinowski, Simon
A2 - Bagnall, Anthony
A2 - Bondu, Alexis
A2 - Guyet, Thomas
A2 - Tavenard, Romain
PB - Springer
CY - Cham
ER -
