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Anomalies are infrequent in nature, but detecting these anomalies could be crucial for the proper functioning of any system. The rarity of anomalies could be a challenge for their detection as detection models are required to depend on the relations of the datapoints with their adjacent datapoints. In this work, we use the rarity of anomalies to detect them. For this, we introduce the reversible instance normalized anomaly transformer (RINAT). Rooted in the foundational principles of the anomaly transformer, RINAT incorporates both prior and series associations for each time point. The prior association uses a learnable Gaussian kernel to ensure a thorough understanding of the adjacent concentration inductive bias. In contrast, the series association method uses self-attention techniques to specifically focus on the original raw data. Furthermore, because anomalies are rare in nature, we utilize normalized data to identify series associations and employ non-normalized data to uncover prior associations. This approach enhances the modelled series associations and, consequently, improves the association discrepancies.