Autoformer
Autoformer is another model that is designed for long-term forecasting. While the Informer model focuses on making the attention computation more efficient, Autoformer invents a new kind of attention and couples it with aspects from time series decomposition.
The architecture of the Autoformer model
Autoformer has a lot of similarities with the Informer model, so much so that it can be thought of as an extension of the Informer model. Uniform Input Representation and the generative-style decoder have been reused in Autoformer. But instead of ProbSparse attention, Autoformer has an AutoCorrelation mechanism. And instead of attention distillation, Autoformer has a time series decomposition-inspired encoder-decoder setup.
Reference check
The research paper by Wu et al. on Autoformer is cited in the References section as 9.
Let’s look at the time series decomposition architecture first.
