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Imbalanced Time Series Classification for Flight Data Analyzing with Nonlinear Granger Causality Learning

Authors:

Feng XueAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 2533 - 2540

https://doi.org/10.1145/3340531.3412710

Published: 19 October 2020 Publication History

Abstract

Identifying the faulty class of multivariate time series is crucial for today's flight data analysis. However, most of the existing time series classification methods suffer from imbalanced data and lack of model interpretability, especially on flight data of which faulty events are usually uncommon with a limited amount of data. Here, we present a neural network classification model for imbalanced multivariate time series by leveraging the information learned from normal class, which can also learn the nonlinear Granger causality for each class, so that we can pinpoint how time series classes differ from each other. Experiments on simulated data and real flight data shows that this model can achieve high accuracy of identifying anomalous flights.

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MP4 File (3340531.3412710.mp4)

Identifying the faulty class of multivariate time series is crucial for today?s flight data analysis. However, most of the existing time series classification methods suffer from imbalanced data and lack of model interpretability, especially on flight data of which faulty events are usually uncommon with a limited amount of data. Here, we present a neural network classification model for imbalanced multivariate time series by leveraging the information learned from normal class, which can also learn the nonlinear Granger causality for each class, so that we can pinpoint how time series classes differ from each other. Experiments on simulated data and real flight data shows that this model can achieve high accuracy of identifying anomalous flights.

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Cited By

Gong CZhang CYao DBi JLi WXu Y(2024)Causal Discovery from Temporal Data: An Overview and New PerspectivesACM Computing Surveys10.1145/370529757:4(1-38)Online publication date: 23-Nov-2024
https://dl.acm.org/doi/10.1145/3705297
Li XShang JZheng LWang QLiu DLiu XLi FCao WSun H(2024)IMTCN: An Interpretable Flight Safety Analysis and Prediction Model Based on Multi-Scale Temporal Convolutional NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330898825:1(289-302)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TITS.2023.3308988
Zhong YLiu TFang FGe JXu BZhao X(2024)Hard Landing Pattern Recognition and Precaution With QAR Data by Functional Data AnalysisIEEE Transactions on Aerospace and Electronic Systems10.1109/TAES.2024.338791960:4(5101-5113)Online publication date: Aug-2024
https://doi.org/10.1109/TAES.2024.3387919
Show More Cited By

Index Terms

Imbalanced Time Series Classification for Flight Data Analyzing with Nonlinear Granger Causality Learning
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

Copyright © 2020 ACM.

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Published: 19 October 2020

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October 19 - 23, 2020

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Gong CZhang CYao DBi JLi WXu Y(2024)Causal Discovery from Temporal Data: An Overview and New PerspectivesACM Computing Surveys10.1145/370529757:4(1-38)Online publication date: 23-Nov-2024
https://dl.acm.org/doi/10.1145/3705297
Li XShang JZheng LWang QLiu DLiu XLi FCao WSun H(2024)IMTCN: An Interpretable Flight Safety Analysis and Prediction Model Based on Multi-Scale Temporal Convolutional NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330898825:1(289-302)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TITS.2023.3308988
Zhong YLiu TFang FGe JXu BZhao X(2024)Hard Landing Pattern Recognition and Precaution With QAR Data by Functional Data AnalysisIEEE Transactions on Aerospace and Electronic Systems10.1109/TAES.2024.338791960:4(5101-5113)Online publication date: Aug-2024
https://doi.org/10.1109/TAES.2024.3387919
Zhou ZJin YLi Y(2023)Rts: learning robustly from time series data with noisy labelFrontiers of Computer Science10.1007/s11704-023-3200-z18:6Online publication date: 28-Dec-2023
https://doi.org/10.1007/s11704-023-3200-z
Huang HYoo S(2023)Dynamic Variable Dependency Encoding and Its Application on Change Point DetectionAdvances in Knowledge Discovery and Data Mining10.1007/978-3-031-33383-5_27(341-352)Online publication date: 26-May-2023
https://doi.org/10.1007/978-3-031-33383-5_27
Wang PWang XWang BZhang YBai LWang Y(2023)Long-Tailed Time Series Classification via Feature Space RebalancingDatabase Systems for Advanced Applications10.1007/978-3-031-30637-2_10(151-166)Online publication date: 14-Apr-2023
https://doi.org/10.1007/978-3-031-30637-2_10
Theissler ASpinnato FSchlegel UGuidotti R(2022)Explainable AI for Time Series Classification: A Review, Taxonomy and Research DirectionsIEEE Access10.1109/ACCESS.2022.320776510(100700-100724)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3207765
Setiawan BSerdült UKryssanov V(2021)A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data StreamsSensors10.3390/s2120689221:20(6892)Online publication date: 18-Oct-2021
https://doi.org/10.3390/s21206892
Pan XZhu BLiu YHuang F(2021)A LSTM-cBiGAN based hybrid sampling method for time series customer classification2021 4th International Conference on Data Science and Information Technology10.1145/3478905.3478921(74-77)Online publication date: 23-Jul-2021
https://dl.acm.org/doi/10.1145/3478905.3478921

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