Transformer-based models have emerged as powerful tools for multivariate time
series forecasting (MTSF). However, existing Transformer models often fall
short of capturing both intricate dependencies across variate and temporal
dimensions in MTS data. Some recent models are proposed to separately capture
variate and temporal dependencies through either two sequential or parallel
attention mechanisms. However, these methods cannot directly and explicitly
learn the intricate inter-series and intra-series dependencies. In this work,
we first demonstrate that these dependencies are very important as they usually
exist in real-world data. To directly model these dependencies, we propose a
transformer-based model UniTST containing a unified attention mechanism on the
flattened patch tokens. Additionally, we add a dispatcher module which reduces
the complexity and makes the model feasible for a potentially large number of
variates. Although our proposed model employs a simple architecture, it offers
compelling performance as shown in our extensive experiments on several
datasets for time series forecasting.

通过提出一种名为 UniTST 的基于 Transformer 的模型，该模型含有统一的关注机制和调度模块，可以直接建模时间序列数据中的复杂依赖关系，并在多个数据集上展现了出色的性能。