Harnessing Symmetry in Recurrence Plots: A Multi-Scale Detail Boosting Approach for Time Series Similarity Measurement

Time series similarity measurement is a fundamental task underpinning clustering, classification, and anomaly detection. Traditional approaches predominantly rely on one-dimensional data representations, which often fail to capture complex structural dependencies. To address this limitation, this paper proposes a novel similarity measurement framework based on two-dimensional image enhancement. The method initially transforms one-dimensional time series into recurrence plots (RPs), converting temporal dynamics into visually symmetric textures, enhancing the temporal information of the one-dimensional time series. To overcome the potential blurring of fine-grained information during transformation, multi-scale detail boosting (MSDB) is introduced to amplify the high-frequency components and textural details of the RP images. Subsequently, a pre-trained ResNet-18 network is utilized to extract deep visual features from the enhanced images, and the similarity is quantified using the Euclidean distance of these feature vectors. Extensive experiments on the UCR Time Series Classification Archive demonstrate that the proposed method effectively leverages image enhancement to reveal latent temporal patterns. This approach leverages the inherent symmetry properties embedded in recurrence plots. By enhancing the texture of these symmetrical structures, the proposed method provides a more robust and informative basis for similarity assessment.