first commit

models/DLinear.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from layers.Autoformer_EncDec import series_decomp
+
+
+class Model(nn.Module):
+    """
+    Paper link: https://arxiv.org/pdf/2205.13504.pdf
+    """
+
+    def __init__(self, configs, individual=False):
+        """
+        individual: Bool, whether shared model among different variates.
+        """
+        super(Model, self).__init__()
+        self.task_name = configs.task_name
+        self.seq_len = configs.seq_len
+        if self.task_name == 'classification' or self.task_name == 'anomaly_detection' or self.task_name == 'imputation':
+            self.pred_len = configs.seq_len
+        else:
+            self.pred_len = configs.pred_len
+        # Series decomposition block from Autoformer
+        self.decompsition = series_decomp(configs.moving_avg)
+        self.individual = 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))
+
+                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)
+
+            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]))
+
+        if self.task_name == 'classification':
+            self.projection = nn.Linear(
+                configs.enc_in * configs.seq_len, configs.num_class)
+
+    def encoder(self, x):
+        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)
+
+    def forecast(self, x_enc):
+        # Encoder
+        return self.encoder(x_enc)
+
+    def imputation(self, x_enc):
+        # Encoder
+        return self.encoder(x_enc)
+
+    def anomaly_detection(self, x_enc):
+        # Encoder
+        return self.encoder(x_enc)
+
+    def classification(self, x_enc):
+        # Encoder
+        enc_out = self.encoder(x_enc)
+        # Output
+        # (batch_size, seq_length * d_model)
+        output = enc_out.reshape(enc_out.shape[0], -1)
+        # (batch_size, num_classes)
+        output = self.projection(output)
+        return output
+
+    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
+        if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
+            dec_out = self.forecast(x_enc)
+            return dec_out[:, -self.pred_len:, :]  # [B, L, D]
+        if self.task_name == 'imputation':
+            dec_out = self.imputation(x_enc)
+            return dec_out  # [B, L, D]
+        if self.task_name == 'anomaly_detection':
+            dec_out = self.anomaly_detection(x_enc)
+            return dec_out  # [B, L, D]
+        if self.task_name == 'classification':
+            dec_out = self.classification(x_enc)
+            return dec_out  # [B, N]
+        return None