MADS: Modulated Auto-Decoding SIREN for time series imputation

TL;DR

MADS is a novel neural network framework that uses implicit representations and hypernetworks to improve time series imputation, outperforming existing methods on real-world datasets.

Contribution

Introduces MADS, a new auto-decoding approach combining SIRENs and hypernetworks for flexible and high-fidelity time series imputation.

Findings

Outperforms state-of-the-art methods on real-world datasets.

Improves imputation accuracy by at least 40% on human activity data.

Achieves best average rank across synthetic datasets.

Abstract

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.