timesead.models.reconstruction.fedformer
========================================

.. py:module:: timesead.models.reconstruction.fedformer


Classes
-------

.. autoapisummary::

   timesead.models.reconstruction.fedformer.FEDformer


Module Contents
---------------

.. py:class:: FEDformer(window_size: int, input_dim: int, moving_avg: int = 25, model_dim: int = 128, dropout: float = 0.1, num_heads: int = 8, fcn_dim: int = 128, activation: str = 'gelu', encoder_layers: int = 3, version: str = 'fourier', mode_select: str = 'random', modes: int = 32)

   Bases: :py:obj:`timesead.models.BaseModel`


   FEDformer performs the attention mechanism on frequency domain and achieved O(N) complexity
   Paper link: https://proceedings.mlr.press/v162/zhou22g.html

   version: str, for FEDformer, there are two versions to choose, options: [Fourier, Wavelets].
   mode_select: str, for FEDformer, there are two mode selection method, options: [random, low].
   modes: int, modes to be selected.


   .. py:attribute:: seq_len


   .. py:attribute:: version
      :value: 'fourier'



   .. py:attribute:: mode_select
      :value: 'random'



   .. py:attribute:: modes
      :value: 32



   .. py:attribute:: decomp


   .. py:attribute:: enc_embedding


   .. py:attribute:: encoder


   .. py:attribute:: projection


   .. py:method:: forward(inputs: Tuple[torch.Tensor, Ellipsis]) -> Tuple[torch.Tensor, Ellipsis]