File size: 3,884 Bytes
093b0a5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | # Code is from https://github.com/cure-lab/LTSF-Linear
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class moving_avg(nn.Module):
"""
Moving average block to highlight the trend of time series
"""
def __init__(self, kernel_size, stride):
super(moving_avg, self).__init__()
self.kernel_size = kernel_size
self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)
def forward(self, x):
# padding on the both ends of time series
front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)
end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)
x = torch.cat([front, x, end], dim=1)
x = self.avg(x.permute(0, 2, 1))
x = x.permute(0, 2, 1)
return x
class series_decomp(nn.Module):
"""
Series decomposition block
"""
def __init__(self, kernel_size):
super(series_decomp, self).__init__()
self.moving_avg = moving_avg(kernel_size, stride=1)
def forward(self, x):
moving_mean = self.moving_avg(x)
res = x - moving_mean
return res, moving_mean
class Model(nn.Module):
"""
Decomposition-Linear
"""
def __init__(self, configs):
super(Model, self).__init__()
self.seq_len = configs.seq_len
self.pred_len = configs.pred_len
# Decompsition Kernel Size
kernel_size = 25
self.decompsition = series_decomp(kernel_size)
self.individual = configs.individual
self.channels = configs.enc_in
if self.individual:
self.Linear_Seasonal = nn.ModuleList()
self.Linear_Trend = nn.ModuleList()
for i in range(self.channels):
self.Linear_Seasonal.append(nn.Linear(self.seq_len, self.pred_len))
self.Linear_Trend.append(nn.Linear(self.seq_len, self.pred_len))
# Use this two lines if you want to visualize the weights
# self.Linear_Seasonal[i].weight = nn.Parameter((1/self.seq_len)*torch.ones([self.pred_len,self.seq_len]))
# self.Linear_Trend[i].weight = nn.Parameter((1/self.seq_len)*torch.ones([self.pred_len,self.seq_len]))
else:
self.Linear_Seasonal = nn.Linear(self.seq_len, self.pred_len)
self.Linear_Trend = nn.Linear(self.seq_len, self.pred_len)
# Use this two lines if you want to visualize the weights
# self.Linear_Seasonal.weight = nn.Parameter((1/self.seq_len)*torch.ones([self.pred_len,self.seq_len]))
# self.Linear_Trend.weight = nn.Parameter((1/self.seq_len)*torch.ones([self.pred_len,self.seq_len]))
def forward(self, x, *args):
# x: [Batch, Input length, Channel]
seasonal_init, trend_init = self.decompsition(x)
seasonal_init, trend_init = seasonal_init.permute(0, 2, 1), trend_init.permute(
0, 2, 1
)
if self.individual:
seasonal_output = torch.zeros(
[seasonal_init.size(0), seasonal_init.size(1), self.pred_len],
dtype=seasonal_init.dtype,
).to(seasonal_init.device)
trend_output = torch.zeros(
[trend_init.size(0), trend_init.size(1), self.pred_len],
dtype=trend_init.dtype,
).to(trend_init.device)
for i in range(self.channels):
seasonal_output[:, i, :] = self.Linear_Seasonal[i](
seasonal_init[:, i, :]
)
trend_output[:, i, :] = self.Linear_Trend[i](trend_init[:, i, :])
else:
seasonal_output = self.Linear_Seasonal(seasonal_init)
trend_output = self.Linear_Trend(trend_init)
x = seasonal_output + trend_output
return x.permute(0, 2, 1) # to [Batch, Output length, Channel]
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