Abstract: Time series data is widely used in fields such as power load forecasting and meteorological analysis. Extracting high-quality representations is crucial for downstream prediction tasks. However, the performance of existing methods is limited by high-frequency noise interference, difficulty in modeling long-term dependencies, and the scarcity of labels. Therefore, a time series representation method based on Spectral Filtering and Hierarchical Dilation (SFHD) was proposed. Firstly, a Spectral Filtering Block (SFB) was designed to extract multi-scale features through global and local filters, and an adaptive spectral filtering mechanism was used in the frequency domain to weaken the influence of high-frequency noise. Then, a Hierarchical Dilation Block (HDB) was constructed, in which exponentially dilated convolutions were utilized to obtain a progressively enlarged receptive field, thereby enhancing the ability to capture long-term dependencies. Finally, a change-aware self-supervised pretraining strategy was proposed, in which highly dynamic segments were masked to force the model to learn the underlying structure of the sequence, thereby alleviating the problem of insufficient labeled data. Experimental results on seven public datasets demonstrate that, compared with the suboptimal model iTransformer, SFHD achieves an average reduction of 9.47% in Mean Square Error (MSE) and 5.36% in Mean Absolute Error (MAE). These results indicate that SFHD provides stronger representation capabilities and leads to improved performance on downstream time series prediction tasks.

Key words: time series, representation learning, filters, dilated convolution, self-supervision, time series prediction

摘要： 时间序列数据在电力负荷预测、气象分析等领域广泛应用，提炼高质量表示对下游预测任务至关重要。然而，高频噪声干扰、长期依赖建模困难和标记稀缺的问题限制了现有方法的性能。为此，提出一种基于频谱滤波和层次化扩张的时间序列表示方法(SFHD)。首先，设计频谱滤波块(SFB)，通过全局与局部滤波器提取多尺度特征，并在频域采用自适应频谱滤波机制，以削弱高频噪声影响；其次，构建层次化扩张块(HDB)，利用指数膨胀卷积结构，以逐层扩大的感受野，提升对长期依赖关系的捕获能力；最后，提出变化感知的自监督预训练策略，通过掩蔽高动态变化数据块，迫使模型理解序列的潜在结构，从而缓解标记不足的问题。在7个公开数据集上的实验结果表明，相较于次优模型iTransformer，SFHD实现了均方误差（MSE）指标平均下降9.47%，平均绝对误差（MAE）指标平均下降5.36%。以上结果验证了SFHD具有更强的表征能力，对下游时间序列预测任务的表现有所提升。

关键词: 时间序列, 表示学习, 滤波器, 扩张卷积, 自监督, 时间序列预测