<ul><li>Researchers propose a transformer-based framework called TransDe for multi-scale time series anomaly detection.</li><li>TransDe combines time series decomposition and transformers to effectively model complex patterns in normal time series data.</li><li>A multi-scale patch-based transformer architecture is used to capture dependencies of each decomposed component of the time series.</li><li>TransDe outperforms twelve baselines in terms of F1 score in extensive experiments on five public datasets.</li></ul>

Decomposition-based multi-scale transformer framework for time series anomaly detection

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