Multivariate time series present challenges to standard machine learning
techniques, as they are often unlabeled, high dimensional, noisy, and contain
missing data. To address this, we propose T-Rep, a self-supervised method to
learn time series representations at a timestep granularity. T-Rep learns
vector embeddings of time alongside its feature extractor, to extract temporal
features such as trend, periodicity, or distribution shifts from the signal.
These time-embeddings are leveraged in pretext tasks, to incorporate smooth and
fine-grained temporal dependencies in the representations, as well as reinforce
robustness to missing data. We evaluate T-Rep on downstream classification,
forecasting, and anomaly detection tasks. It is compared to existing
self-supervised algorithms for time series, which it outperforms in all three
tasks. We test T-Rep in missing data regimes, where it proves more resilient
than its counterparts. Finally, we provide latent space visualisation
experiments, highlighting the interpretability of the learned representations.

多变量时间序列的自我监督学习方法 T-Rep 能够学习时间序列的表示，并提取出趋势、周期性、分布变化等时间特征。在分类、预测和异常检测任务中，T-Rep 表现优于现有的自我监督学习算法，并在处理缺失数据时表现更强韧。此外，通过潜空间可视化实验证明了学到的表示的可解释性。