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Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks

Author

Listed:
  • St'ephane Cr'epey

    (LPSM)

  • Lehdili Noureddine

    (LPSM)

  • Nisrine Madhar

    (LPSM)

  • Maud Thomas

    (LPSM)

Abstract

A major concern when dealing with financial time series involving a wide variety ofmarket risk factors is the presence of anomalies. These induce a miscalibration of the models used toquantify and manage risk, resulting in potential erroneous risk measures. We propose an approachthat aims to improve anomaly detection in financial time series, overcoming most of the inherentdifficulties. Valuable features are extracted from the time series by compressing and reconstructingthe data through principal component analysis. We then define an anomaly score using a feedforwardneural network. A time series is considered to be contaminated when its anomaly score exceeds agiven cutoff value. This cutoff value is not a hand-set parameter but rather is calibrated as a neuralnetwork parameter throughout the minimization of a customized loss function. The efficiency of theproposed approach compared to several well-known anomaly detection algorithms is numericallydemonstrated on both synthetic and real data sets, with high and stable performance being achievedwith the PCA NN approach. We show that value-at-risk estimation errors are reduced when theproposed anomaly detection model is used with a basic imputation approach to correct the anomaly.

Suggested Citation

  • St'ephane Cr'epey & Lehdili Noureddine & Nisrine Madhar & Maud Thomas, 2022. "Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks," Papers 2209.11686, arXiv.org, revised Oct 2022.
  • Handle: RePEc:arx:papers:2209.11686
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    References listed on IDEAS

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    2. Erdinc Akyildirim & Matteo Gambara & Josef Teichmann & Syang Zhou, 2022. "Applications of Signature Methods to Market Anomaly Detection," Papers 2201.02441, arXiv.org, revised Feb 2022.
    3. Yufeng Yu & Yuelong Zhu & Shijin Li & Dingsheng Wan, 2014. "Time Series Outlier Detection Based on Sliding Window Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-14, October.
    4. Pierre Henry-Labordere, 2019. "(Martingale) Optimal Transport And Anomaly Detection With Neural Networks: A Primal-dual Algorithm," Papers 1904.04546, arXiv.org, revised Apr 2019.
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    Cited by:

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    2. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.

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