MADS: Modulated Auto-Decoding SIREN for time series imputation
This addresses the challenge of imputing missing data in time series for fields like activity monitoring and environmental sensing, representing an incremental advance over existing deep learning methods.
The authors tackled time series imputation by proposing MADS, a novel auto-decoding framework using implicit neural representations, which outperformed state-of-the-art methods, achieving at least a 40% improvement on a human activity dataset and competitive results on an air quality dataset.
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.