Time series observations can be seen as realizations of an underlying dynamical system governed by rules that we typically do not know. For time series learning tasks, we need to understand that we fit our model on available data, which is a unique realized history. Training on a single realization often induces severe overfitting lacking generalization. To address this issue, we introduce a general recursive framework for time series augmentation, which we call Recursive Interpolation Method, denoted as RIM. New samples are generated using a recursive interpolation function of all previous values in such a way that the enhanced samples preserve the original inherent time series dynamics. We perform theoretical analysis to characterize the proposed RIM and to guarantee its test performance. We apply RIM to diverse real world time series cases to achieve strong performance over non-augmented data on regression, classification, and reinforcement learning tasks.
翻译:时间序列观测可被视为实现由我们通常不知道的规则所规范的基本动态系统。对于时间序列学习任务,我们需要理解的是,我们把模型与现有数据相匹配,这是一个独特的已实现历史。关于单一实现的培训往往导致严重过度缺乏概括性。为解决这一问题,我们引入了一个时间序列增强的一般循环框架,我们称之为 " 递归性内插方法 ",称为RIM。新样本的生成利用了所有先前数值的循环内插功能,使强化样本能够保存原始固有的时间序列动态。我们进行理论分析,以描述拟议的RIM的特点,并保证其测试性能。我们将RIM应用于不同的实时时间序列案例,以便在非强化的回归、分类和强化学习任务数据的基础上取得强劲的性能。