Researchers have proposed a new method for forecasting irregular time series data, which is commonly used in healthcare and meteorological observation. The Debiased Neural Basis-Function Network (DNBNet) addresses two key limitations of existing methods by correcting asymptotic bias through importance sampling and adapting to diverse temporal patterns using neural networks. A novel multi-scale decomposition module and mass-aware fusion mechanism are also designed to obtain richer representations. Experiments on multiple real-world datasets demonstrate the effectiveness of DNBNet. --- Why it matters: This matters because it provides a more accurate way to forecast irregular time series data, which is crucial in many domains such as healthcare and meteorological observation. The proposed method can adapt to diverse temporal patterns and correct asymptotic bias, making it a valuable tool for researchers and practitioners working with this type of data. Source: https://arxiv.org/abs/2608.17284