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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.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1014389","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T17:46:41Z","timestamp":1781286401000},"page":"e1014389","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["A new method for augmenting short time series, with application to pain events in sickle cell 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