Continuous-time Autoencoders for Regular and Irregular Time Series Imputation

• URL: http://arxiv.org/abs/2312.16581v3
• Date: Mon, 24 Jun 2024 06:53:23 GMT
• Title: Continuous-time Autoencoders for Regular and Irregular Time Series Imputation
• Authors: Hyowon Wi, Yehjin Shin, Noseong Park,
• Abstract summary: Time series imputation is one of the most fundamental tasks for time series. Recent self-attention-based methods show the state-of-the-art imputation performance. It has been overlooked for a long time to design an imputation method based on continuous-time recurrent neural networks.
• Score: 21.25279298572273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series imputation is one of the most fundamental tasks for time series. Real-world time series datasets are frequently incomplete (or irregular with missing observations), in which case imputation is strongly required. Many different time series imputation methods have been proposed. Recent self-attention-based methods show the state-of-the-art imputation performance. However, it has been overlooked for a long time to design an imputation method based on continuous-time recurrent neural networks (RNNs), i.e., neural controlled differential equations (NCDEs). To this end, we redesign time series (variational) autoencoders based on NCDEs. Our method, called continuous-time autoencoder (CTA), encodes an input time series sample into a continuous hidden path (rather than a hidden vector) and decodes it to reconstruct and impute the input. In our experiments with 4 datasets and 19 baselines, our method shows the best imputation performance in almost all cases.

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