HyperIMTS：用于不规则多元时间序列预测的超图神经网络
HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting
AI总结：
针对不规则多元时间序列中时间间隔不规则和观测未对齐的挑战，提出基于超图神经网络的HyperIMTS模型，通过时间和变量超边统一表示依赖关系，实现准确预测并降低计算成本。
摘要：
不规则多元时间序列（IMTS）的特点是变量内部的时间间隔不规则，以及变量之间的观测未对齐，这给学习时间依赖和变量依赖带来了挑战。许多现有的IMTS模型要么需要填充样本以分别从时间和变量维度学习，要么通过二分图或集合来表示原始样本。然而，前一类方法通常需要处理额外的填充值，这会影响效率并破坏原始采样模式，而后一类方法在捕捉未对齐观测之间的依赖关系方面存在局限性。为了以统一的形式表示和学习原始观测中的两种依赖关系，我们提出了HyperIMTS，一种用于不规则多元时间序列预测的超图神经网络。观测值被转换为超图中的节点，通过时间和变量超边相互连接，以实现所有观测之间的消息传递。通过感知不规则性的消息传递，HyperIMTS以时间自适应的方式捕捉变量依赖，从而实现准确的预测。实验表明，HyperIMTS在IMTS预测中与最先进的模型相比具有竞争力的性能，且计算成本较低。
英文摘要：
Irregular multivariate time series (IMTS) are characterized by irregular time intervals within variables and unaligned observations across variables, posing challenges in learning temporal and variable dependencies. Many existing IMTS models either require padded samples to learn separately from temporal and variable dimensions, or represent original samples via bipartite graphs or sets. However, the former approaches often need to handle extra padding values affecting efficiency and disrupting original sampling patterns, while the latter ones have limitations in capturing dependencies among unaligned observations. To represent and learn both dependencies from original observations in a unified form, we propose HyperIMTS, a Hypergraph neural network for Irregular Multivariate Time Series forecasting. Observed values are converted as nodes in the hypergraph, interconnected by temporal and variable hyperedges to enable message passing among all observations. Through irregularity-aware message passing, HyperIMTS captures variable dependencies in a time-adaptive way to achieve accurate forecasting. Experiments demonstrate HyperIMTS's competitive performance among state-of-the-art models in IMTS forecasting with low computational cost.
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