Long-Range Transformers for Dynamic Spatiotemporal Forecasting. (arXiv:2109.12218v2 [cs.LG] UPDATED)

Multivariate Time Series Forecasting focuses on the prediction of future
values based on historical context. State-of-the-art sequence-to-sequence
models rely on neural attention between timesteps, which allows for temporal
learning but fails to consider distinct spatial relationships between
variables. In contrast, methods based on graph neural networks explicitly model
variable relationships. However, these methods often rely on predefined graphs
and perform separate spatial and temporal updates without establishing direct
connections between each variable at every timestep. This paper addresses these
problems …

arxiv forecasting transformers