Self-supervised learning (SSL) is a powerful paradigm for learning from unlabeled time-series data. However, popular methods such as masked autoencoders (MAEs) rely on reconstructing inputs from a fixed, predetermined masking ratio. Instead of this static design, we propose treating the corruption level as a new degree of freedom for representation learning, enhancing flexibility and performance. To achieve this, we introduce the Flow-Guided Neural Operator (FGNO), a novel framework combining operator learning with flow matching for SSL training. FGNO learns mappings in functional spaces by using Short-Time Fourier Transform to unify different time resolutions. We extract a rich hierarchy of features by tapping into different network layers and flow times that apply varying strengths of noise to the input data. This enables the extraction of versatile representations, from low-level patterns to high-level global features, using a single model adaptable to specific tasks. Unlike prior generative SSL methods that use noisy inputs during inference, we propose using clean inputs for representation extraction while learning representations with noise; this eliminates randomness and boosts accuracy. We evaluate FGNO across three biomedical domains, where it consistently outperforms established baselines. Our method yields up to 35% AUROC gains in neural signal decoding (BrainTreeBank), 16% RMSE reductions in skin temperature prediction (DREAMT), and over 20% improvement in accuracy and macro-F1 on SleepEDF under low-data regimes. These results highlight FGNO's robustness to data scarcity and its superior capacity to learn expressive representations for diverse time series.

翻译：自监督学习（SSL）是一种从无标签时间序列数据中学习的强大范式。然而，诸如掩码自编码器（MAEs）等流行方法依赖于从固定、预定的掩码比率重建输入。与这种静态设计不同，我们提出将破坏程度视为表示学习的一个新自由度，以增强灵活性和性能。为实现这一目标，我们引入了流引导神经算子（FGNO），这是一个将算子学习与流匹配相结合用于SSL训练的新颖框架。FGNO通过使用短时傅里叶变换来统一不同的时间分辨率，从而在函数空间中学习映射。我们通过利用不同的网络层和流时间（这些层和时间对输入数据施加不同强度的噪声）来提取丰富的特征层次结构。这使得能够使用一个可适应特定任务的单一模型，提取从低级模式到高级全局特征的多样化表示。与先前在推理过程中使用噪声输入的生成式SSL方法不同，我们建议在表示提取时使用干净输入，同时在训练中使用噪声学习表示；这消除了随机性并提高了准确性。我们在三个生物医学领域评估了FGNO，其表现始终优于现有基线方法。我们的方法在神经信号解码（BrainTreeBank）中实现了高达35%的AUROC增益，在皮肤温度预测（DREAMT）中实现了16%的RMSE降低，并且在低数据量情况下，在SleepEDF数据集上的准确率和宏F1分数提高了超过20%。这些结果突显了FGNO对数据稀缺性的鲁棒性及其为多样化时间序列学习表达性表示的卓越能力。