feat: add TimesNet_Q and xPatch models with Q matrix transformations

layers/dema.py

@@ -0,0 +1,27 @@
+import torch
+from torch import nn
+
+class DEMA(nn.Module):
+    """
+    Double Exponential Moving Average (DEMA) block to highlight the trend of time series
+    """
+    def __init__(self, alpha, beta):
+        super(DEMA, self).__init__()
+        # self.alpha = nn.Parameter(alpha)    # Learnable alpha
+        # self.beta = nn.Parameter(beta)      # Learnable beta
+        self.alpha = alpha.to(device='cuda')
+        self.beta = beta.to(device='cuda')
+
+    def forward(self, x):
+        # self.alpha.data.clamp_(0, 1)        # Clamp learnable alpha to [0, 1]
+        # self.beta.data.clamp_(0, 1)         # Clamp learnable beta to [0, 1]
+        s_prev = x[:, 0, :]
+        b = x[:, 1, :] - s_prev
+        res = [s_prev.unsqueeze(1)]
+        for t in range(1, x.shape[1]):
+            xt = x[:, t, :]
+            s = self.alpha * xt + (1 - self.alpha) * (s_prev + b)
+            b = self.beta * (s - s_prev) + (1 - self.beta) * b
+            s_prev = s
+            res.append(s.unsqueeze(1))
+        return torch.cat(res, dim=1)