频率掩码嵌入推理：一种用于时间序列表示学习的非对比方法
Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning
AI总结：
针对对比学习依赖正负样本且难以建模时间序列连续语义的问题，本文提出非对比方法 FEI，通过双向频率掩码提示与嵌入推理学习表示，并在8个数据集上验证其泛化优势。
摘要：
对比学习支撑着当前大多数自监督时间序列表示方法。构建正样本对和负样本对的策略会显著影响最终的表示质量。然而，由于时间序列语义具有连续性，对比学习的建模方式难以适应时间序列数据的特性。这导致了一些问题，例如难以构建难负样本，以及在正样本构建过程中可能引入不恰当的偏差。尽管近期一些工作已经提出了若干用于构建正样本对和负样本对的科学策略，并提升了有效性，但它们仍然受到对比学习框架的限制。为了从根本上克服对比学习的局限，本文提出了 Frequency-masked Embedding Inference（FEI，频率掩码嵌入推理），这是一种全新的非对比方法，完全不需要正样本和负样本。所提出的 FEI 基于提示策略构建了两个推理分支：1）使用频率掩码作为提示，在嵌入空间中推理缺失频段的目标序列的嵌入表示；2）使用目标序列作为提示，推理其频率掩码嵌入。通过这种方式，FEI 实现了时间序列的连续语义关系建模。在8个广泛使用的时间序列数据集上，针对分类和回归任务采用线性评估和端到端微调进行的实验表明，FEI 在泛化能力方面显著优于现有基于对比的方法。本研究为时间序列的自监督表示学习提供了新的见解。代码可在 https://github.com/USTBInnovationPark/Frequency-masked-Embedding-Inference 获取。
英文摘要：
Contrastive learning underpins most current self-supervised time series representation methods. The strategy for constructing positive and negative sample pairs significantly affects the final representation quality. However, due to the continuous nature of time series semantics, the modeling approach of contrastive learning struggles to accommodate the characteristics of time series data. This results in issues such as difficulties in constructing hard negative samples and the potential introduction of inappropriate biases during positive sample construction. Although some recent works have developed several scientific strategies for constructing positive and negative sample pairs with improved effectiveness, they remain constrained by the contrastive learning framework. To fundamentally overcome the limitations of contrastive learning, this paper introduces Frequency-masked Embedding Inference (FEI), a novel non-contrastive method that completely eliminates the need for positive and negative samples. The proposed FEI constructs 2 inference branches based on a prompting strategy: 1) Using frequency masking as prompts to infer the embedding representation of the target series with missing frequency bands in the embedding space, and 2) Using the target series as prompts to infer its frequency masking embedding. In this way, FEI enables continuous semantic relationship modeling for time series. Experiments on 8 widely used time series datasets for classification and regression tasks, using linear evaluation and end-to-end fine-tuning, show that FEI significantly outperforms existing contrastive-based methods in terms of generalization. This study provides new insights into self-supervised representation learning for time series. The code is available at https://github.com/USTBInnovationPark/Frequency-masked-Embedding-Inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
