Autoformer

Information

• Autoformer is a variation of the Transformer architecture designed for long-term series forecasting.
• Autoformer uses an autoregressive model, which means that it uses previous values of the time series as inputs to predict future values.
• In addition to the standard self-attention mechanism in the Transformer, Autoformer introduces an auto-correlation attention mechanism, which considers the correlation between past and future values of the time series.
• The auto-correlation attention mechanism in Autoformer helps to capture long-term dependencies in the time series, which is often challenging for traditional forecasting methods.
• Autoformer also includes a decomposition mechanism that decomposes the input time series into its trend, seasonality, and residual components, making it easier for the model to learn from the different components separately.
• The trend and seasonality components are then fed into the auto-correlation attention mechanism, while the residual component is fed into the standard self-attention mechanism.
• Autoformer has been shown to outperform other state-of-the-art forecasting methods on several benchmark datasets.