Spectral-adaptive masking and hierarchical contrastive learning for time series clustering

Abstract

Time series clustering is a pivotal technique for efficiently mining the structure of data. However, time series data possess unique characteristics such as periodicity, nonlinearity and sensitivity to high-frequency noise in dynamic environments, which significantly impact the performance of clustering methods. Recently, deep clustering has garnered widespread attention for its outstanding performance in capture multi-scale temporal dependencies. Despite this, existing methods struggle to effectively captured the similarities and diverse temporal patterns in noisy time series. Accordingly, we propose a novel deep time series clustering framework integrating spectral-adaptive masking with hierarchical contrastive learning. First, an enhanced encoder is designed to generate representations of time series through the incorporation of a frequency-domain adaptive noise filter, which dynamically suppress high-frequency fluctuations by learning threshold parameters from spectral power distributions. Second, hierarchical contrastive information is captured at both the temporal-level alignment within overlapping segments and instance-level comparisons across augmented subsequences, while simultaneously performing clustering on low-dimensional space and utilizing a novel fuzzy clustering loss to improve robustness against outlier interference. Finally, the network architecture is optimized through the integration of contrastive loss and clustering loss, which achieves end-to-end joint representation learning and clustering assignment. Extensive experiments on various time series datasets demonstrate that our approach outperforms state-of-the-art clustering methods.