timesead.models.reconstruction.timesnet

Classes

`TimesBlock`	Base class for all neural network modules.
`TimesNet`	Paper link: https://openreview.net/pdf?id=ju_Uqw384Oq

Functions

FFT_for_Period(→ Tuple[torch.tensor, torch.tensor])

Module Contents

timesead.models.reconstruction.timesnet.FFT_for_Period(x: torch.tensor, k: int = 2) → Tuple[torch.tensor, torch.tensor]

Parameters:

x (torch.tensor)
k (int)

Return type:

Tuple[torch.tensor, torch.tensor]

class timesead.models.reconstruction.timesnet.TimesBlock(window_size: int, top_k: int = 5, d_model: int = 64, d_ff: int = 64, num_kernels: int = 8)

Bases: 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 to(), etc.

Note

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

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Parameters:

window_size (int)
top_k (int)
d_model (int)
d_ff (int)
num_kernels (int)

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

seq_len

top_k = 5

conv

forward(x: torch.tensor) → torch.tensor

Parameters:: x (torch.tensor)
Return type:: torch.tensor

class timesead.models.reconstruction.timesnet.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: timesead.models.BaseModel

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

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

Parameters:

window_size (int)
input_dim (int)
top_k (int)
d_model (int)
d_ff (int)
num_kernels (int)
e_layers (int)
dropout (float)

seq_len

model

enc_embedding

layer = 2

layer_norm

projection

forward(inputs: Tuple[torch.Tensor, Ellipsis]) → Tuple[torch.Tensor, Ellipsis]

Parameters:: inputs (Tuple[torch.Tensor, Ellipsis])
Return type:: Tuple[torch.Tensor, Ellipsis]