从基函数角度重新思考非规则时间序列预测

中文摘要 AI 辅助

非规则时间序列预测在医疗、气象观测等诸多领域至关重要。但由于非规则时间序列的固有特性，包括观测稀疏、采样非均匀，准确预测未来动态仍具挑战性。针对这两个特性，现有诸多方法通过预定义基函数将非规则观测聚合为固定维数的估计响应系数，并将这些系数作为序列表示。然而，该建模范式仍存在两个关键局限：（i）因忽略时间戳采样密度可能导致的非消失渐近偏差；（ii）预定义基函数对多样时间模式的适应性有限。本研究提出去偏神经基函数网络（DNBNet）以应对这些挑战，其核心为去偏神经基函数响应机制，该机制通过重要性采样校正渐近偏差，同时用神经网络参数化基函数以适配多样时间模式。此外，考虑到非规则数据的稀疏性，设计了基于平均池化的新型多尺度分解模块，结合质量感知融合机制以获取更丰富的表示，最终采用双分支解码器进行预测。在多个真实世界数据集上的大量实验证明了DNBNet的有效性及其在多样非规则时间序列场景中的强泛化性，代码可在指定网址获取。

英文摘要

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.

发表机构

• College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与电子工程学院）

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