用简单模型提升不规则时间序列预测精度与效率
Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
- 设计自适应分块模块，动态调整时间片段边界并加权平均
- 在多个真实数据集上优于现有最优方法，且计算开销更低
- 适合医疗、气候等需高效精准预测的场景
不规则多变量时间序列（IMTS）预测在医疗、生物力学、气候科学和天文学等领域至关重要。然而，由于序列固有的不规则性和数据缺失，建模难度大，且多数现有方法复杂且资源消耗高。本文提出通用框架APN，设计新型时间感知分块聚合（TAPA）模块，通过学习动态可调的分块边界和时间感知加权平均策略，以通道无关方式将原始不规则序列转化为高质量规整表示。同时，采用简洁查询模块有效整合历史信息，保持模型高效；最终由浅层MLP进行预测。多组真实数据集实验表明，APN在准确率和效率上均优于现有最先进方法。
原文摘要 · Abstract (English)
The forecasting of irregular multivariate time series (IMTS) is crucial in key areas such as healthcare, biomechanics, climate science, and astronomy. However, achieving accurate and practical predictions is challenging due to two main factors. First, the inherent irregularity and data missingness in irregular time series make modeling difficult. Second, most existing methods are typically complex and resource-intensive. In this study, we propose a general framework called APN to address these challenges. Specifically, we design a novel Time-Aware Patch Aggregation (TAPA) module that achieves adaptive patching. By learning dynamically adjustable patch boundaries and a time-aware weighted averaging strategy, TAPA transforms the original irregular sequences into high-quality, regularized representations in a channel-independent manner. Additionally, we use a simple query module to effectively integrate historical information while maintaining the model's efficiency. Finally, predictions are made by a shallow MLP. Experimental results on multiple real-world datasets show that APN outperforms existing state-of-the-art methods in both efficiency and accuracy.
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