论文标题
时空身份：多元时间序列预测的简单而有效的基线
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting
论文作者
论文摘要
多元时间序列（MTS）预测在广泛的应用中起着至关重要的作用。最近，由于其最先进的性能，空间 - 周期性图神经网络（STGNN）已成为越来越流行的MTS预测方法。但是，随着绩效的有限改善，最近的工作变得越来越复杂。这种现象促使我们探索MTS预测和设计模型的关键因素，该模型与STGNN一样强大，但更简洁，更有效。在本文中，我们确定了在空间和时间维度中样本的不可区分性作为关键瓶颈，并通过将空间和时间身份信息（STID）（STID）（同时基于简单的多人多层Perceptrons（MLPS）（MLPS）（MLPS）（MLPS）（MLPS）（MLPS）（MLPS）（MLPS）（MLPS）（MLPS）（MLPS）提出了简单而有效的基线。这些结果表明，只要它们解决样品的不可区分性而不受限制在STGNN中，我们就可以设计高效有效的模型。
Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods due to their state-of-the-art performance. However, recent works are becoming more sophisticated with limited performance improvements. This phenomenon motivates us to explore the critical factors of MTS forecasting and design a model that is as powerful as STGNNs, but more concise and efficient. In this paper, we identify the indistinguishability of samples in both spatial and temporal dimensions as a key bottleneck, and propose a simple yet effective baseline for MTS forecasting by attaching Spatial and Temporal IDentity information (STID), which achieves the best performance and efficiency simultaneously based on simple Multi-Layer Perceptrons (MLPs). These results suggest that we can design efficient and effective models as long as they solve the indistinguishability of samples, without being limited to STGNNs.
