Using Transformer Models for Stock Market Anomaly Detection
Anomaly detection is an important task in financial markets. Detecting anomalies is difficult due to their rarity, multitude of parameters, and lack of labeled data for supervised learning models. Additionally, time series data used in financial models present unique challenges such as irregul...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
INTI International University
2023
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1848/ http://eprints.intimal.edu.my/1848/1/jods2023_21.pdf |
| Summary: | Anomaly detection is an important task in financial markets. Detecting anomalies is difficult due
to their rarity, multitude of parameters, and lack of labeled data for supervised learning models.
Additionally, time series data used in financial models present unique challenges such as
irregularity, seasonality, changing trends, and periodicity in data. While prior anomaly detection
approaches have used ARIMA and LSTM models, in this paper, we employ a new transformer�based model called TranAD to compare stock market data with its predicted version, measuring
deviations from normal price data for anomaly detection. We find that TranAD is an effective
approach for financial anomaly detection with a high level of accuracy. We expect that this
research will contribute to better detection of financial anomalies and improve market surveillance |
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