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Life sciences · Preprint
arXiv · August 18, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint proposes DNBNet, a neural basis-function network designed to forecast irregular time series by correcting asymptotic bias through importance sampling and adapting basis functions via neural networks. The work addresses a known technical challenge in time series modeling but remains unrefereed and lacks peer review; no clinical evidence or real-world outcome validation is reported.
Preprint. Intervention: Debiased Neural Basis-Function Network (DNBNet) for irregular time series forecasting.
Method addresses two limitations of existing approaches: non-vanishing asymptotic bias from ignoring sampling density, and limited adaptability of predefined basis functions Proposes debiased neural basis-function response mechanism with importance sampling correction Includes multi-scale decomposition module and mass-aware fusion mechanism to handle sparse irregular data
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This is an unrefereed arXiv preprint describing a novel machine learning method for irregular time series forecasting, with computational experiments but no clinical trial, regulatory decision, or peer-reviewed publication reported.
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Irregular time series forecasting is crucial in many domains, such as healthcare and meteorological observation. However, due to the inherent characteristics of irregular time series, including sparse observations and non-uniform sampling, accurately predicting future dynamics remains challenging. In light of these two characteristics, many existing methods aggregate irregular observations into fixed-dimensional estimated response coefficients through predefined basis functions and use these coefficients as sequence representations. Nevertheless, this modeling paradigm still suffers from two key limitations: (i) a potential non-vanishing asymptotic bias caused by ignoring the sampling density of timestamps; and (ii) the limited adaptability of predefined basis functions to diverse temporal patterns. In this study, we propose a Debiased Neural Basis-Function Network (DNBNet) to address these challenges. Its core is a debiased neural basis-function response mechanism, which corrects asymptotic bias through importance sampling while parameterizing basis functions with neural networks to adapt to diverse temporal patterns. In addition, considering the sparsity of irregular data, we design a novel multi-scale decomposition module based on average pooling, together with a mass-aware fusion mechanism, to obtain richer representations. Finally, a dual-branch decoder is employed for forecasting. Extensive experiments on multiple real-world datasets demonstrate the effectiveness of DNBNet and its strong generalizability across diverse irregular time series scenarios. Our code can be obtained at https://github.com/hnu-vis/DNBNet.
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