Time series widely exists in real-world applications and many deep learning models have performed well on it. Current research has shown the importance of learning strategy for models, suggesting that the benefit is the order and size of learning samples. However, no effective strategy has been proposed for time series due to its abstract and dynamic construction. Meanwhile, the existing one-shot tasks and continuous tasks for time series necessitate distinct learning processes and mechanisms. No all-purpose approach has been suggested. In this work, we propose a novel Curricular and CyclicaL loss (CRUCIAL) to learn time series for the first time. It is model- and task-agnostic and can be plugged on top of the original loss with no extra procedure. CRUCIAL has two characteristics: It can arrange an easy-to-hard learning order by dynamically determining the sample contribution and modulating the loss amplitude; It can manage a cyclically changed dataset and achieve an adaptive cycle by correlating the loss distribution and the selection probability. We prove that compared with monotonous size, cyclical size can reduce expected error. Experiments on 3 kinds of tasks and 5 real-world datasets show the benefits of CRUCIAL for most deep learning models when learning time series.

翻译：时间序列广泛存在于现实世界应用中，众多深度学习模型已在其上取得良好表现。当前研究揭示了学习策略对模型的重要性，其优势在于学习样本的顺序与规模。然而，由于时间序列的抽象性与动态构建特性，尚未有有效的策略被提出。同时，针对时间序列的现有一次性任务与连续任务需要不同的学习过程与机制，而通用性方法至今缺位。本文首次提出一种新型的课程化与周期损失（CRUCIAL），用于学习时间序列。该方法具有模型与任务无关性，可无缝嵌入原始损失函数，无需额外流程。CRUCIAL具备两大特性：通过动态确定样本贡献并调节损失幅度，实现由易到难的学习顺序；通过关联损失分布与选择概率，管理周期性变化的数据集并实现自适应循环。我们理论证明，相较于单调化规模，周期性规模能降低期望误差。在3类任务与5个真实数据集上的实验表明，CRUCIAL在学习时间序列时对大多数深度学习模型均具有显著优势。