Using Transformer Models for Stock Market Anomaly Detection

Biriukova, Kseniia and Bhattacherjee, Anol (2023) Using Transformer Models for Stock Market Anomaly Detection. Journal of Data Science, 2023 (21). pp. 1-8. ISSN 2805-5160

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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

Item Type: Article
Uncontrolled Keywords: Anomaly detection, transformers, financial markets, deep learning
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA76 Computer software
Depositing User: Unnamed user with email
Date Deposited: 05 Dec 2023 03:54
Last Modified: 05 Dec 2023 03:54

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