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Implicit neural representations (INRs) have recently emerged as a powerfultool that provides an accurate and resolution-independent encoding of data.Their robustness as general approximators has been shown in a wide variety ofdata sources, with applications on image, sound, and 3D scene representation.However, little attention has been given to leveraging these architectures forthe representation and analysis of time series data. In this paper, we analyzethe representation of time series using INRs, comparing different activationfunctions in terms of reconstruction accuracy and training convergence speed.We show how these networks can be leveraged for the imputation of time series,with applications on both univariate and multivariate data. Finally, we proposea hypernetwork architecture that leverages INRs to learn a compressed latentrepresentation of an entire time series dataset. We introduce an FFT-based lossto guide training so that all frequencies are preserved in the time series. Weshow that this network can be used to encode time series as INRs, and theirembeddings can be interpolated to generate new time series from existing ones.We evaluate our generative method by using it for data augmentation, and showthat it is competitive against current state-of-the-art approaches foraugmentation of time series.

HyperTime: Implicit Neural Representation for Time Series