Set Functions for Time Series
This addresses a challenge in healthcare applications where time series data is often irregular, offering a scalable and efficient solution, though it appears incremental as it builds on existing set function learning.
The paper tackles the problem of classifying irregularly-sampled and asynchronous time series, common in healthcare, by proposing SeFT, a method based on differentiable set functions that scales well and reduces runtime while performing competitively on multiple datasets.
Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications. This paper proposes a novel approach for classifying irregularly-sampled time series with unaligned measurements, focusing on high scalability and data efficiency. Our method SeFT (Set Functions for Time Series) is based on recent advances in differentiable set function learning, extremely parallelizable with a beneficial memory footprint, thus scaling well to large datasets of long time series and online monitoring scenarios. Furthermore, our approach permits quantifying per-observation contributions to the classification outcome. We extensively compare our method with existing algorithms on multiple healthcare time series datasets and demonstrate that it performs competitively whilst significantly reducing runtime.