  Multivariate time series forecasting tasks are usually conducted in a
channel-dependent (CD) way since it can incorporate more variable-relevant
information. However, it may also involve a lot of irrelevant variables, and
this even leads to worse performance than the channel-independent (CI)
strategy. This paper combines the strengths of both strategies and proposes the
Deep Graph Clustering Transformer (DGCformer) for multivariate time series
forecasting. Specifically, it first groups these relevant variables by a graph
convolutional network integrated with an autoencoder, and a former-latter
masked self-attention mechanism is then considered with the CD strategy being
applied to each group of variables while the CI one for different groups.
Extensive experimental results on eight datasets demonstrate the superiority of
our method against state-of-the-art models, and our code will be publicly
available upon acceptance.
