We propose the Signal Dice Similarity Coefficient (SDSC), a structure-aware metric function for time series self-supervised representation learning. Most Self-Supervised Learning (SSL) methods for signals commonly adopt distance-based objectives such as mean squared error (MSE), which are sensitive to amplitude, invariant to waveform polarity, and unbounded in scale. These properties hinder semantic alignment and reduce interpretability. SDSC addresses this by quantifying structural agreement between temporal signals based on the intersection of signed amplitudes, derived from the Dice Similarity Coefficient (DSC).Although SDSC is defined as a structure-aware metric, it can be used as a loss by subtracting from 1 and applying a differentiable approximation of the Heaviside function for gradient-based optimization. A hybrid loss formulation is also proposed to combine SDSC with MSE, improving stability and preserving amplitude where necessary. Experiments on forecasting and classification benchmarks demonstrate that SDSC-based pre-training achieves comparable or improved performance over MSE, particularly in in-domain and low-resource scenarios. The results suggest that structural fidelity in signal representations enhances the semantic representation quality, supporting the consideration of structure-aware metrics as viable alternatives to conventional distance-based methods.

翻译：本文提出信号Dice相似系数（SDSC），一种用于时间序列自监督表示学习的结构感知度量函数。现有大多数信号自监督学习方法通常采用基于距离的目标函数（如均方误差MSE），这类方法对幅值敏感、对波形极性不变且尺度无界，这些特性会阻碍语义对齐并降低可解释性。SDSC通过基于Dice相似系数推导的符号幅值交集来量化时序信号间的结构一致性，从而解决上述问题。尽管SDSC被定义为结构感知度量，但通过从1中减去该值并应用Heaviside函数的可微分近似，可将其转化为适用于梯度优化的损失函数。本文还提出一种混合损失公式，将SDSC与MSE相结合，在必要时提升训练稳定性并保留幅值信息。在预测和分类基准测试上的实验表明，基于SDSC的预训练取得了与MSE相当或更优的性能，尤其在领域内和低资源场景中表现突出。结果表明，信号表示中的结构保真度能提升语义表示质量，这支持将结构感知度量视为传统基于距离方法的可行替代方案。