221 lines
9.0 KiB
Python
221 lines
9.0 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.fft
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import numpy as np
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import os
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from layers.Embed import DataEmbedding
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from layers.Conv_Blocks import Inception_Block_V1
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def FFT_for_Period(x, k=2):
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# [B, T, C]
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xf = torch.fft.rfft(x, dim=1)
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# find period by amplitudes
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frequency_list = abs(xf).mean(0).mean(-1)
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frequency_list[0] = 0
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_, top_list = torch.topk(frequency_list, k)
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top_list = top_list.detach().cpu().numpy()
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period = x.shape[1] // top_list
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return period, abs(xf).mean(-1)[:, top_list]
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class TimesBlock(nn.Module):
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"""Original TimesBlock without Q matrix transformation"""
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def __init__(self, configs):
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super(TimesBlock, self).__init__()
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self.seq_len = configs.seq_len
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self.pred_len = configs.pred_len
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self.k = configs.top_k
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# parameter-efficient design
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self.conv = nn.Sequential(
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Inception_Block_V1(configs.d_model, configs.d_ff,
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num_kernels=configs.num_kernels),
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nn.GELU(),
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Inception_Block_V1(configs.d_ff, configs.d_model,
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num_kernels=configs.num_kernels)
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)
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def forward(self, x):
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B, T, N = x.size()
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period_list, period_weight = FFT_for_Period(x, self.k)
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res = []
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for i in range(self.k):
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period = period_list[i]
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# padding
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if (self.seq_len + self.pred_len) % period != 0:
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length = (
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((self.seq_len + self.pred_len) // period) + 1) * period
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padding = torch.zeros([x.shape[0], (length - (self.seq_len + self.pred_len)), x.shape[2]]).to(x.device)
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out = torch.cat([x, padding], dim=1)
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else:
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length = (self.seq_len + self.pred_len)
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out = x
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# reshape
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out = out.reshape(B, length // period, period,
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N).permute(0, 3, 1, 2).contiguous()
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# 2D conv: from 1d Variation to 2d Variation
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out = self.conv(out)
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# reshape back
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out = out.permute(0, 2, 3, 1).reshape(B, -1, N)
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res.append(out[:, :(self.seq_len + self.pred_len), :])
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res = torch.stack(res, dim=-1)
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# adaptive aggregation
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period_weight = F.softmax(period_weight, dim=1)
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period_weight = period_weight.unsqueeze(
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1).unsqueeze(1).repeat(1, T, N, 1)
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res = torch.sum(res * period_weight, -1)
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# residual connection
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res = res + x
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return res
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class Model(nn.Module):
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"""
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TimesNet with Q matrix transformation
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- Applies input Q matrix transformation before embedding
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- Uses original TimesBlock logic
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- Applies output Q matrix transformation before De-Normalization
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Only implements long/short term forecasting
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"""
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def __init__(self, configs):
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super(Model, self).__init__()
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self.configs = configs
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self.task_name = configs.task_name
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self.seq_len = configs.seq_len
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self.label_len = configs.label_len
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self.pred_len = configs.pred_len
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# Load Q matrices
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self.load_Q_matrices(configs)
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# Model layers
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self.model = nn.ModuleList([TimesBlock(configs)
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for _ in range(configs.e_layers)])
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self.enc_embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq,
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configs.dropout)
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self.layer = configs.e_layers
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self.layer_norm = nn.LayerNorm(configs.d_model)
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# Only implement forecast-related layers
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self.predict_linear = nn.Linear(
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self.seq_len, self.pred_len + self.seq_len)
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self.projection = nn.Linear(
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configs.d_model, configs.c_out, bias=True)
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def load_Q_matrices(self, configs):
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"""Load pre-computed Q matrices for input and output transformations"""
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# Get dataset name from configs, default to ETTm1 if not specified
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dataset_name = getattr(configs, 'dataset', 'ETTm1')
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# Input Q matrix (seq_len)
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input_q_path = f'cov_mats/{dataset_name}/{dataset_name}_{configs.seq_len}_ratio1.0.npy'
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# Output Q matrix (pred_len)
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output_q_path = f'cov_mats/{dataset_name}/{dataset_name}_{configs.pred_len}_ratio1.0.npy'
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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if os.path.exists(input_q_path):
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Q_input = np.load(input_q_path)
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self.register_buffer('Q_input', torch.FloatTensor(Q_input).to(device))
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print(f"Loaded input Q matrix from {input_q_path}, shape: {Q_input.shape}")
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else:
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print(f"Warning: Input Q matrix not found at {input_q_path}, using identity matrix")
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self.register_buffer('Q_input', torch.eye(configs.seq_len).to(device))
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if os.path.exists(output_q_path):
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Q_output = np.load(output_q_path)
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self.register_buffer('Q_output', torch.FloatTensor(Q_output).to(device))
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print(f"Loaded output Q matrix from {output_q_path}, shape: {Q_output.shape}")
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else:
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print(f"Warning: Output Q matrix not found at {output_q_path}, using identity matrix")
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self.register_buffer('Q_output', torch.eye(configs.pred_len).to(device))
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def apply_input_Q_transformation(self, x):
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"""
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Apply input Q matrix transformation before embedding
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Input: x with shape [B, T, N] where T = seq_len
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Output: transformed x with shape [B, T, N]
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"""
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B, T, N = x.size()
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# Transpose to [B, N, T] for matrix multiplication
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x_transposed = x.transpose(-1, -2) # [B, N, T]
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# Apply input Q transformation: einsum 'bnt,tv->bnv'
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# x_transposed: [B, N, T], Q_input.T: [T, T] -> result: [B, N, T]
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x_trans = torch.einsum('bnt,tv->bnv', x_transposed, self.Q_input.transpose(-1, -2))
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# Transpose back to [B, T, N]
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x_transformed = x_trans.transpose(-1, -2) # [B, T, N]
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return x_transformed
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def apply_output_Q_transformation(self, x):
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"""
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Apply output Q matrix transformation to prediction output
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Input: x with shape [B, pred_len, N]
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Output: transformed x with shape [B, pred_len, N]
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"""
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B, T, N = x.size()
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assert T == self.pred_len, f"Expected pred_len {self.pred_len}, got {T}"
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# Transpose to [B, N, T] for matrix multiplication
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x_transposed = x.transpose(-1, -2) # [B, N, pred_len]
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# Apply output Q transformation: einsum 'bnt,tv->bnv'
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# x_transposed: [B, N, pred_len], Q_output: [pred_len, pred_len] -> result: [B, N, pred_len]
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x_trans = torch.einsum('bnt,tv->bnv', x_transposed, self.Q_output)
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# Transpose back to [B, pred_len, N]
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x_transformed = x_trans.transpose(-1, -2) # [B, pred_len, N]
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return x_transformed
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def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
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# Normalization from Non-stationary Transformer
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means = x_enc.mean(1, keepdim=True).detach()
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x_enc = x_enc.sub(means)
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stdev = torch.sqrt(
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torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
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x_enc = x_enc.div(stdev)
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# Apply input Q matrix transformation before embedding
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x_enc_transformed = self.apply_input_Q_transformation(x_enc)
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# embedding with transformed input
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enc_out = self.enc_embedding(x_enc_transformed, x_mark_enc) # [B,T,C]
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enc_out = self.predict_linear(enc_out.permute(0, 2, 1)).permute(
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0, 2, 1) # align temporal dimension
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# TimesNet blocks (original logic, no Q transformation)
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for i in range(self.layer):
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enc_out = self.layer_norm(self.model[i](enc_out))
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# project back
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dec_out = self.projection(enc_out)
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# Extract prediction part and apply output Q transformation
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pred_out = dec_out[:, -self.pred_len:, :] # [B, pred_len, N]
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pred_out_transformed = self.apply_output_Q_transformation(pred_out)
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# De-Normalization from Non-stationary Transformer
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pred_out_transformed = pred_out_transformed.mul(
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(stdev[:, 0, :].unsqueeze(1).repeat(
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1, self.pred_len, 1)))
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pred_out_transformed = pred_out_transformed.add(
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(means[:, 0, :].unsqueeze(1).repeat(
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1, self.pred_len, 1)))
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return pred_out_transformed
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def forward(self, x_enc, x_mark_enc=None, x_dec=None, x_mark_dec=None, mask=None):
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# Only support long_term_forecast and short_term_forecast
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if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
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dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
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return dec_out # [B, pred_len, D]
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else:
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raise NotImplementedError(f"Task {self.task_name} is not implemented in TimesNet_Q")
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return None |