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.

Decomposition architecture

We saw this idea of decomposition...