timesead.models.reconstruction.timesnet
=======================================

.. py:module:: timesead.models.reconstruction.timesnet


Classes
-------

.. autoapisummary::

   timesead.models.reconstruction.timesnet.TimesBlock
   timesead.models.reconstruction.timesnet.TimesNet


Functions
---------

.. autoapisummary::

   timesead.models.reconstruction.timesnet.FFT_for_Period


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

.. py:function:: FFT_for_Period(x: torch.tensor, k: int = 2) -> Tuple[torch.tensor, torch.tensor]

.. py:class:: TimesBlock(window_size: int, top_k: int = 5, d_model: int = 64, d_ff: int = 64, num_kernels: int = 8)

   Bases: :py:obj:`torch.nn.Module`


   Base class for all neural network modules.

   Your models should also subclass this class.

   Modules can also contain other Modules, allowing them to be nested in
   a tree structure. You can assign the submodules as regular attributes::

       import torch.nn as nn
       import torch.nn.functional as F

       class Model(nn.Module):
           def __init__(self) -> None:
               super().__init__()
               self.conv1 = nn.Conv2d(1, 20, 5)
               self.conv2 = nn.Conv2d(20, 20, 5)

           def forward(self, x):
               x = F.relu(self.conv1(x))
               return F.relu(self.conv2(x))

   Submodules assigned in this way will be registered, and will also have their
   parameters converted when you call :meth:`to`, etc.

   .. note::
       As per the example above, an ``__init__()`` call to the parent class
       must be made before assignment on the child.

   :ivar training: Boolean represents whether this module is in training or
                   evaluation mode.
   :vartype training: bool

   Initialize internal Module state, shared by both nn.Module and ScriptModule.


   .. py:attribute:: seq_len


   .. py:attribute:: top_k
      :value: 5



   .. py:attribute:: conv


   .. py:method:: forward(x: torch.tensor) -> torch.tensor


.. py:class:: TimesNet(window_size: int, input_dim: int, top_k: int = 5, d_model: int = 64, d_ff: int = 64, num_kernels: int = 8, e_layers: int = 2, dropout: float = 0.1)

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


   Paper link: https://openreview.net/pdf?id=ju_Uqw384Oq

   Initialize internal Module state, shared by both nn.Module and ScriptModule.


   .. py:attribute:: seq_len


   .. py:attribute:: model


   .. py:attribute:: enc_embedding


   .. py:attribute:: layer
      :value: 2



   .. py:attribute:: layer_norm


   .. py:attribute:: projection


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