Curriculum learning and imitation learning have been leveraged extensively inthe robotics domain. However, minimal research has been done on leveragingthese ideas on control tasks over highly stochastic time-series data. Here, wetheoretically and empirically explore these approaches in a representativecontrol task over complex time-series data. We implement the fundamental ideasof curriculum learning via data augmentation, while imitation learning isimplemented via policy distillation from an oracle. Our findings reveal thatcurriculum learning should be considered a novel direction in improvingcontrol-task performance over complex time-series. Our ample random-seedout-sample empirics and ablation studies are highly encouraging for curriculumlearning for time-series control. These findings are especially encouraging aswe tune all overlapping hyperparameters on the baseline -- giving an advantageto the baseline. On the other hand, we find that imitation learning should beused with caution.