Transformer for Point Anomaly Detection

In data analysis, unsupervised anomaly detection holds an important position for identifying statistical outliers that signify atypical behavior, erroneous readings, or interesting patterns within data. The Transformer model, known for its ability to capture dependencies within sequences, has revolutionized areas such as text and image data analysis. However, its potential for tabular data, where sequence dependencies are not inherently present, remains underexplored. This paper introduces Transformer for Point Anomaly Detection (TransPAD), a novel Transformer-based AutoEncoder framework specifically designed for point anomaly detection. Our method captures interdependencies across entire datasets, addressing the challenges posed with non-sequential, tabular data. It incorporates unique random and criteria sampling strategies for effective training and anomaly identification, and avoids the common pitfall of trivial generalization that affects many conventional methods. By leveraging an attention weight-based anomaly scoring system, TransPAD offers a more precise approach to detect anomalies. Extensive testing on a range of benchmark tabular datasets shows that TransPAD consistently outperforms existing methods. Our source code is available at https://github.com/nth221/TransPAD.