A new method for augmenting short time series, with application to pain events in sickle cell disease

TL;DR

This paper introduces a novel data augmentation method that combines similar sparse time series datasets to improve modeling accuracy, demonstrated through applications to pain dynamics in sickle cell disease patients.

Contribution

A new data augmentation technique for sparse time series that enhances parameter estimation and model selection, validated through simulations and real-world sickle cell disease data.

Findings

Improved accuracy in modeling pain dynamics in SCD patients.

Enhanced parameter estimation in Hawkes and Poisson process models.

Validated approach through simulation and real data application.

Abstract

Researchers across different fields, including but not limited to ecology, biology, and healthcare, often face the challenge of sparse data. Such sparsity can lead to uncertainties, estimation difficulties, and potential biases in modeling. Here we introduce a novel data augmentation method that combines multiple sparse time series datasets when they share similar statistical properties, thereby improving parameter estimation and model selection reliability. We demonstrate the effectiveness of this approach through validation studies comparing Hawkes and Poisson processes, followed by application to subjective pain dynamics in patients with sickle cell disease (SCD), a condition affecting millions worldwide, particularly those of African, Mediterranean, Middle Eastern, and Indian descent.