feat: add DiffusionTimeSeries and iTransformer models, introduce xPatch_SparseChannel

models/xPatch_SparseChannel/xPatch_SparseChannel.py

@@ -0,0 +1,63 @@
+import torch
+from torch import nn
+from .patchtst_ci import SeasonPatch           # <<< 新导入
+
+class Network(nn.Module):
+    """
+    trend  : 原 MLP 线性流 (完全保留)
+    season : SeasonPatch (PatchTST + Mixer)
+    """
+    def __init__(self, seq_len, pred_len,
+                 patch_len, stride, padding_patch,
+                 c_in):
+        super().__init__()
+
+        # -------- 季节性流 ---------------
+        self.season_net = SeasonPatch(c_in=c_in,
+                                      seq_len=seq_len,
+                                      pred_len=pred_len,
+                                      patch_len=patch_len,
+                                      stride=stride,
+                                      k_graph=5,
+                                      d_model=128)
+
+        # --------- 线性趋势流 (原代码保持不变) ----------
+        self.pred_len = pred_len
+        self.fc5 = nn.Linear(seq_len, pred_len * 4)
+        self.avgpool1 = nn.AvgPool1d(kernel_size=2)
+        self.ln1 = nn.LayerNorm(pred_len * 2)
+
+        self.fc6 = nn.Linear(pred_len * 2, pred_len)
+        self.avgpool2 = nn.AvgPool1d(kernel_size=2)
+        self.ln2 = nn.LayerNorm(pred_len // 2)
+
+        self.fc7 = nn.Linear(pred_len // 2, pred_len)
+
+        # 流结果拼接
+        self.fc_final = nn.Linear(pred_len * 2, pred_len)
+
+    # ---------------- forward --------------------
+    def forward(self, s, t):
+        # 输入形状:  [B,L,C]
+        B,L,C = s.shape
+
+        # ---------- Seasonality ------------
+        y_season = self.season_net(s)                # [B,C,T]
+
+        # ---------- Trend (原 MLP) ----------
+        t = t.permute(0,2,1).reshape(B*C, L)         # [B*C,L]
+        t = self.fc5(t)
+        t = self.avgpool1(t)
+        t = self.ln1(t)
+        t = self.fc6(t)
+        t = self.avgpool2(t)
+        t = self.ln2(t)
+        t = self.fc7(t)                              # [B*C,T]
+        y_trend = t.view(B, C, -1)    # [B,C,T]
+
+        # --------- 拼接 & 输出 --------------
+        y = torch.cat([y_season, y_trend], dim=-1)    # [B,C,2T]
+        y = self.fc_final(y)          # [B,C,T]
+        y = y.permute(0,2,1)          # [B,T,C]
+        return y
+