Enhanced recurrent convolutional encoding with attention-based representation learning for chaotic time series anomaly detection

Abstract Chaotic time series pose significant challenges for anomaly detection tasks due to their highly nonlinear characteristics. Traditional methods struggle to accurately simulate their dynamic evolution, effectively handle chaotic noise, and meet the demands of rapid inference, unlabeled datasets, and long-term data persistence, all of which hinder the development of efficient and accurate anomaly detection models. To address these issues, this study proposes an unsupervised anomaly detection method based on Empirical Mode Decomposition (EMD) and Recurrent Convolutional Encoding Attention mechanism (RC-Attention). This method first reconstructs the original sequence from high to low frequencies using an improved EMD to mitigate high-frequency noise interference. Then, it reconstructs the phase space of the decomposed sequence and inputs the multi-dimensional array into the network. To fit the time-dependent relationship, a recurrent convolutional encoding attention mechanism is introduced to mine chaotic sequence features: recurrent convolutional encoding converts long sequences into short sequences with a tower structure and upsampling encoding, retaining key information; the central stationary attention mechanism learns temporal relationships and reduces the tendency to learn abnormal states; and stacked large-kernel convolution learns the correlation between embedding dimensions. Compared to baseline methods, RC-Attention effectively avoids overfitting on abnormal states and significantly improves generalization ability and time-dependent relationship extraction ability. Experiments on Lorenz, Rossler, and energy consumption datasets show that this model can efficiently recover and reconstruct chaotic sequence features, with an average F1 score improvement of 14.9%, validating its effectiveness and superiority.