混合它：探索Mixer网络用于不规则多变量时间序列预测
Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting
AI总结：
本文提出IMTS-Mixer网络，通过ISCAM和ConTP组件有效处理不规则多变量时间序列预测，实现高精度与低参数量的平衡。
摘要：
对具有缺失值的不规则多变量时间序列（IMTS）进行预测是医疗、气候科学和生物学等领域中的基本挑战。尽管最近在视觉和时间序列预测中的进展表明，轻量级MLP架构（如MLP-Mixer、TSMixer）在准确性和效率上可以与基于注意力的模型相媲美，但它们在不规则和稀疏时间序列中的适用性仍未被探索。在本文中，我们提出了IMTS-Mixer，一种新的架构，将Mixer模型的原则适应到IMTS设置中。IMTS-Mixer引入了两个关键组件：（1）ISCAM，一种通道式编码器，利用简单的MLP将不规则观测转换为固定大小的向量；（2）ConTP，一种连续时间解码器，支持在任意时间点进行预测。在我们对已建立的基准数据集上的实验中，我们证明了我们的模型在预测准确性和推理时间方面都达到了最先进的性能，同时比基线模型使用更少的参数。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
