  Time series prediction is crucial for understanding and forecasting complex
dynamics in various domains, ranging from finance and economics to climate and
healthcare. Based on Transformer architecture, one approach involves encoding
multiple variables from the same timestamp into a single temporal token to
model global dependencies. In contrast, another approach embeds the time points
of individual series into separate variate tokens. The former method faces
challenges in learning variate-centric representations, while the latter risks
missing essential temporal information critical for accurate forecasting. In
our work, we introduce GridTST, a model that combines the benefits of two
approaches using innovative multi-directional attentions based on a vanilla
Transformer. We regard the input time series data as a grid, where the $x$-axis
represents the time steps and the $y$-axis represents the variates. A vertical
slicing of this grid combines the variates at each time step into a
\textit{time token}, while a horizontal slicing embeds the individual series
across all time steps into a \textit{variate token}. Correspondingly, a
\textit{horizontal attention mechanism} focuses on time tokens to comprehend
the correlations between data at various time steps, while a \textit{vertical},
variate-aware \textit{attention} is employed to grasp multivariate
correlations. This combination enables efficient processing of information
across both time and variate dimensions, thereby enhancing the model's
analytical strength. % We also integrate the patch technique, segmenting time
tokens into subseries-level patches, ensuring that local semantic information
is retained in the embedding. The GridTST model consistently delivers
state-of-the-art performance across various real-world datasets.
