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

2026-01-08

AI Summary

This study addresses the challenges of modeling uncertainty and biased parameter estimation arising from data sparsity in short time series across multiple domains. To overcome the limitations of single-sequence modeling, the authors propose a multi-source time series fusion enhancement method grounded in statistical homogeneity matching. By integrating multiple sparse yet distributionally similar short sequences, the approach jointly models the data using a combination of Hawkes and Poisson processes. Empirical evaluation on pain event data from sickle cell disease patients demonstrates that the proposed method substantially improves the accuracy of parameter estimation and the reliability of model selection, while effectively uncovering the dynamic evolution patterns of patients’ subjective pain experiences.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• sparse data
• short time series
• data augmentation
• pain events
• sickle cell disease

Innovation

• data augmentation
• sparse time series
• Hawkes process
• model selection
• sickle cell disease