Continuous-time Autoencoders for Regular and Irregular Time Series Imputation
This addresses the need for accurate imputation in incomplete real-world time series data, offering a novel approach that improves upon existing methods.
The paper tackled the problem of imputing missing values in regular and irregular time series by proposing a continuous-time autoencoder based on neural controlled differential equations, achieving state-of-the-art performance in almost all cases across 4 datasets and 19 baselines.
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.