Abstract:

Time series data are widely used in fields such as power load forecasting and meteorological analysis. Extracting high-quality representations of time series is crucial for downstream prediction tasks. However， the performance of the 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， so as to weaken the influence of high-frequency noise. Then， a Hierarchical Dilation Block （HDB） was constructed to use exponentially dilated convolutions to enlarge the receptive field progressively， thereby enhancing the ability to capture long-term dependencies. Finally， a change-aware self-supervised pretraining strategy was proposed to force the model to understand underlying structure of the series by masking the highly-dynamic data blocks， thereby alleviating the insufficiency of labeled data. Experimental results on seven public datasets with different prediction lengths demonstrate that， compared with the suboptimal model iTransformer （inverted Transformer）， the average Mean Square Error （MSE） of SFHD decreases by 9.47%， and the average Mean Absolute Error （MAE） decreases by 5.36%. It can be seen that SFHD provides stronger representation capabilities and leads to improved performance on downstream time series prediction tasks.

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

摘要：

时间序列数据在电力负荷预测和气象分析等领域广泛应用，提炼高质量的时间序列表示对下游预测任务至关重要。然而，高频噪声干扰、长期依赖的建模困难和标记稀缺问题限制了现有方法的性能。因此，提出一种基于频谱滤波和层次化扩张（SFHD）的时间序列表示方法。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，从而削弱高频噪声的影响；其次，构建层次化扩张块（HDB），利用指数膨胀卷积结构逐层扩大感受野，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个公开数据集上不同预测长度的实验结果表明，相较于次优模型iTransformer（inverted Transformer），SFHD的均方误差（MSE）指标的平均值下降了9.47%，平均绝对误差（MAE）指标的平均值下降了5.36%。实验结果验证了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