轻量级网络解决不规则多变量时间序列预测难题
Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting
- 用MLP构建通道编码器，将不规则数据转为固定向量
- 支持任意时间点预测，精度和速度均超越现有模型
- 参数少、效率高，适合医疗等稀疏数据场景
不规则采样多变量时间序列（IMTS）的预测是医疗、气候和生物学等领域的重要挑战。尽管视觉与时间序列领域中基于轻量级MLP的架构（如MLP-Mixer、TSMixer）在准确率与效率上已可媲美注意力模型，但其在不规则、稀疏时间序列中的应用尚未探索。本文提出IMTS-Mixer，将Mixer思想适配至IMTS场景。该模型引入两个关键组件：(1) ISCAM——一种通道式编码器，使用简单MLP将不规则观测转换为固定长度向量；(2) ConTP——连续时间解码器，支持在任意时间点进行预测。在多个基准数据集上的实验表明，本模型在预测精度与推理速度上均达当前最优，且参数量低于基线。
原文摘要 · Abstract (English)
Forecasting irregularly sampled multivariate time series with missing values (IMTS) is a fundamental challenge in domains such as healthcare, climate science, and biology. While recent advances in vision and time series forecasting have shown that lightweight MLP-based architectures (e.g., MLP-Mixer, TSMixer) can rival attention-based models in both accuracy and efficiency, their applicability to irregular and sparse time series remains unexplored. In this paper, we propose IMTS-Mixer, a novel architecture that adapts the principles of Mixer models to the IMTS setting. IMTS-Mixer introduces two key components: (1) ISCAM, a channel-wise encoder that transforms irregular observations into fixed-size vectors using simple MLPs, and (2) ConTP, a continuous time decoder that supports forecasting at arbitrary time points. In our experiments on established benchmark datasets we show that our model achieves state-of-the- art performance in both forecasting accuracy and inference time, while using fewer parameters compared to baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。