Contrastive representation learning is crucial in time series analysis as it alleviates the issue of data noise and incompleteness as well as sparsity of supervision signal. However, existing constrastive learning frameworks usually focus on intral-temporal features, which fails to fully exploit the intricate nature of time series data. To address this issue, we propose DE-TSMCL, an innovative distillation enhanced framework for long sequence time series forecasting. Specifically, we design a learnable data augmentation mechanism which adaptively learns whether to mask a timestamp to obtain optimized sub-sequences. Then, we propose a contrastive learning task with momentum update to explore inter-sample and intra-temporal correlations of time series to learn the underlying structure feature on the unlabeled time series. Meanwhile, we design a supervised task to learn more robust representations and facilitate the contrastive learning process. Finally, we jointly optimize the above two tasks. By developing model loss from multiple tasks, we can learn effective representations for downstream forecasting task. Extensive experiments, in comparison with state-of-the-arts, well demonstrate the effectiveness of DE-TSMCL, where the maximum improvement can reach to 27.3%.

翻译：对比表示学习在时间序列分析中至关重要，因为它缓解了数据噪声、不完整性以及监督信号稀疏性问题。然而，现有对比学习框架通常仅关注时序内部特征，未能充分挖掘时间序列数据的复杂特性。为解决此问题，我们提出DE-TSMCL——一种创新的蒸馏增强长序列时间序列预测框架。具体而言，我们设计了一种可学习的数据增强机制，该机制自适应地决定是否掩码时间戳以获取优化的子序列。随后，我们提出基于动量更新的对比学习任务，通过探索时间序列的样本间与时序内部相关性，在无标签时间序列上学习底层结构特征。同时，我们设计监督任务以学习更鲁棒的表示并促进对比学习过程。最后，我们对上述两个任务进行联合优化。通过多任务模型损失设计，我们可为下游预测任务学习有效表示。与最先进方法的广泛实验表明，DE-TSMCL的有效性得到充分验证，最大改进幅度可达27.3%。