  Anomaly detection in time series data is crucial across various domains. The
scarcity of labeled data for such tasks has increased the attention towards
unsupervised learning methods. These approaches, often relying solely on
reconstruction error, typically fail to detect subtle anomalies in complex
datasets. To address this, we introduce RESTAD, an adaptation of the
Transformer model by incorporating a layer of Radial Basis Function (RBF)
neurons within its architecture. This layer fits a non-parametric density in
the latent representation, such that a high RBF output indicates similarity
with predominantly normal training data. RESTAD integrates the RBF similarity
scores with the reconstruction errors to increase sensitivity to anomalies. Our
empirical evaluations demonstrate that RESTAD outperforms various established
baselines across multiple benchmark datasets.
