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

arXiv: https://arxiv.org/abs/2209.11686 · HTML: https://mlsanity.com/paper/2209.11686/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2022.

## Built on by (most cited first, 6 of 6)

- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction](https://mlsanity.com/paper/2310.15416/) (2023)
- [A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection](https://mlsanity.com/paper/2511.18739/) (2025)
- [Optimal Bound for PCA with Outliers using Higher-Degree Voronoi Diagrams](https://mlsanity.com/paper/2408.06867/) (2024)
- [An Interpretable Generative Framework for Anomaly Detection in High-Dimensional Financial Time Series](https://mlsanity.com/paper/2603.07864/) (2026)
- [Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs](https://mlsanity.com/paper/2607.22786/) (2026)

## Abstract (arXiv)

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.