MADS: Modulated Auto-Decoding SIREN for time series imputation
Tom Bamford Elizabeth Fons Yousef El-Laham Svitlana Vyetrenko
Time series imputation remains a significant challenge across many fields due
to the potentially significant variability in the type of data being modelled.
Whilst traditional imputation methods often impose strong assumptions on the
underlying data generation process, limiting their applicability, researchers
have recently begun to investigate the potential of deep learning for this
task, inspired by the strong performance shown by these models in both
classification and regression problems across a range of applications. In this
work we propose MADS, a novel auto-decoding framework for time series
imputation, built upon implicit neural representations. Our method leverages
the capabilities of SIRENs for high fidelity reconstruction of signals and
irregular data, and combines it with a hypernetwork architecture which allows
us to generalise by learning a prior over the space of time series. We evaluate
our model on two real-world datasets, and show that it outperforms
state-of-the-art methods for time series imputation. On the human activity
dataset, it improves imputation performance by at least 40%, while on the air
quality dataset it is shown to be competitive across all metrics. When
evaluated on synthetic data, our model results in the best average rank across
different dataset configurations over all baselines.