现有的时间序列对比学习方法存在增强样本构造方式过于依赖人工经验、泛化能力不足、正样本的定义方式不够通用、对比度量方式存在粗粒度表征等问题,使得整体的时序表示效果较差.为此,提出了一种融合时频特征的多粒度时间序列对比学习方法(Temporal-Spectral Deep Contrastive Network,TSDC).该方法通过季节-趋势生成网络在时域内产生具有稳定变化的时序增强样本,通过多频带融合扰动操作在频域内产生非稳定变化的时序增强样本,两种增强样本通过实例级别的粗粒度对比以及维度级别的细粒度对比方式进行对比学习,使得模型在获得较好表征的同时能够较好地适应于下游不同类型的时序任务.在多个时间序列公开数据集上进行的分类、预测以及异常检测实验表明,由TSDC方法所得的表征用于下游任务的结果优于典型基线模型.
Multi-granularity Time Series Contrastive Learning Method Incorporating Time-Frequency Features
Existing time series contrastive learning methods have some problems,such as augmented sample construction methods rely too much on manual experience,insufficient generalization ability,positive samples are not defined in a general enough way,and coarse-grained representations of contrastive measures,resulting in weak overall time series representation.Therefore,a multi-granularity time series contrastive learning method based on time-frequency features(TSDC)is proposed.The seasonal-trend generation network generates temporal augmentation samples with stable variations in the time domain,and the multi-band fusion perturbation operation generates non-stable variations temporal augmentation samples in the frequency domain,and the two augmentation samples are learned through coarse-grained contrastive at the instance level and fine-grained contrastive at the dimension level,so that the model can be better adapted to different types of downstream time series tasks while obtaining better representation.Experiments on classification,prediction,and anomaly detection on multiple time series public datasets show that the representation obtained by the TSDC method outperforms typical baseline models for downstream tasks.
Time seriesRepresentation learningContrastive learningData augmentationMulti-granularity contrastive
万方数据
维普
