Skip to yearly menu bar Skip to main content

In-Person Poster presentation / poster accept

Recursive Time Series Data Augmentation

Amine Aboussalah · Minjae Kwon · Raj Patel · Cheng Chi · Chi-Guhn Lee

MH1-2-3-4 #93

Keywords: [ time series ] [ data augmentation ] [ representation learning ] [ deep learning ] [ reinforcement learning ] [ Deep Learning and representational learning ]


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 create our model using available data. Training on available realizations, where data is limited, often induces severe over-fitting thereby preventing generalization. To address this issue, we introduce a general recursive framework for time series augmentation, which we call the Recursive Interpolation Method (RIM). New augmented time series are generated using a recursive interpolation function from the original time series for use in training. We perform theoretical analysis to characterize the proposed RIM and to guarantee its performance under certain conditions. We apply RIM to diverse synthetic and real-world time series cases to achieve strong performance over non-augmented data on a variety of learning tasks. Our method is also computationally more efficient and leads to better performance when compared to state of the art time series data augmentation.

Chat is not available.