diff --git a/.gitignore b/.gitignore
index ac0c3ec..8d21011 100644
--- a/.gitignore
+++ b/.gitignore
@@ -160,4 +160,5 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
-STDEN/
\ No newline at end of file
+.STDEN/
+.data/PEMS08/
\ No newline at end of file
diff --git a/STDEN b/STDEN
new file mode 160000
index 0000000..e50a1ba
--- /dev/null
+++ b/STDEN
@@ -0,0 +1 @@
+Subproject commit e50a1ba6d70528b3e684c85f316aed05bb5085f2
diff --git a/configs/STDEN/PEMS08.yaml b/configs/STDEN/PEMS08.yaml
new file mode 100644
index 0000000..14fc617
--- /dev/null
+++ b/configs/STDEN/PEMS08.yaml
@@ -0,0 +1,65 @@
+basic:
+  device: cuda:0
+  dataset: PEMS08
+  model: STDEN
+  mode: train
+  seed: 2025
+
+data:
+  dataset_dir: data/PEMS08
+  val_batch_size: 32
+  graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
+  num_nodes: 170
+  batch_size: 32
+  input_dim: 1
+  lag: 24
+  horizon: 24
+  val_ratio: 0.2
+  test_ratio: 0.2
+  tod: False
+  normalizer: std
+  column_wise: False
+  default_graph: True
+  add_time_in_day: True
+  add_day_in_week: True
+  steps_per_day: 24
+  days_per_week: 7
+
+model:
+  l1_decay: 0
+  seq_len: 12
+  horizon: 12
+  input_dim: 1 
+  output_dim: 1
+  latent_dim: 4
+  n_traj_samples: 3
+  ode_method: dopri5 
+  odeint_atol: 0.00001 
+  odeint_rtol: 0.00001 
+  rnn_units: 64 
+  num_rnn_layers: 1
+  gcn_step: 2
+  filter_type: default # unkP IncP default
+  recg_type: gru 
+  save_latent: false
+  nfe: false
+
+train:
+  loss: mae
+  batch_size: 64
+  epochs: 100
+  lr_init: 0.003
+  mape_thresh: 0.001
+  mae_thresh: None
+  debug: False
+  output_dim: 1
+  weight_decay: 0
+  lr_decay: False
+  lr_decay_rate: 0.3
+  lr_decay_step: "5,20,40,70"
+  early_stop: True
+  early_stop_patience: 15
+  grad_norm: False
+  max_grad_norm: 5
+  real_value: True
+  
\ No newline at end of file
diff --git a/configs/STDEN/stde_gt.yaml b/configs/STDEN/stde_gt.yaml
new file mode 100644
index 0000000..1b2ca18
--- /dev/null
+++ b/configs/STDEN/stde_gt.yaml
@@ -0,0 +1,44 @@
+---
+log_base_dir: logs/BJ_GM
+log_level: INFO
+
+data:
+  batch_size: 32
+  dataset_dir: data/BJ_GM
+  val_batch_size: 32
+  graph_pkl_filename: data/sensor_graph/adj_GM.npy
+
+model:
+  l1_decay: 0
+  seq_len: 12
+  horizon: 12
+  input_dim: 1 
+  output_dim: 1
+  latent_dim: 4
+  n_traj_samples: 3
+  ode_method: dopri5 
+  odeint_atol: 0.00001 
+  odeint_rtol: 0.00001 
+  rnn_units: 64 
+  num_rnn_layers: 1
+  gcn_step: 2
+  filter_type: default # unkP IncP default
+  recg_type: gru 
+  save_latent: false
+  nfe: false
+
+train:
+  base_lr: 0.01
+  dropout: 0
+  load: 0 
+  epoch: 0 
+  epochs: 100
+  epsilon: 1.0e-3
+  lr_decay_ratio: 0.1
+  max_grad_norm: 5
+  min_learning_rate: 2.0e-06
+  optimizer: adam
+  patience: 20
+  steps: [20, 30, 40, 50]
+  test_every_n_epochs: 5
+  
\ No newline at end of file
diff --git a/configs/STDEN/stde_wrs.yaml b/configs/STDEN/stde_wrs.yaml
new file mode 100644
index 0000000..8402736
--- /dev/null
+++ b/configs/STDEN/stde_wrs.yaml
@@ -0,0 +1,44 @@
+---
+log_base_dir: logs/BJ_RM
+log_level: INFO
+
+data:
+  batch_size: 32
+  dataset_dir: data/BJ_RM
+  val_batch_size: 32
+  graph_pkl_filename: data/sensor_graph/adj_RM.npy
+
+model:
+  l1_decay: 0
+  seq_len: 12
+  horizon: 12
+  input_dim: 1
+  output_dim: 1
+  latent_dim: 4
+  n_traj_samples: 3
+  ode_method: dopri5
+  odeint_atol: 0.00001
+  odeint_rtol: 0.00001
+  rnn_units: 64 # for recognition
+  num_rnn_layers: 1 
+  gcn_step: 2 
+  filter_type: default # unkP IncP default
+  recg_type: gru
+  save_latent: false
+  nfe: false
+
+train:
+  base_lr: 0.01
+  dropout: 0
+  load: 0 # 0 for not load
+  epoch: 0
+  epochs: 100
+  epsilon: 1.0e-3
+  lr_decay_ratio: 0.1
+  max_grad_norm: 5
+  min_learning_rate: 2.0e-06
+  optimizer: adam
+  patience: 20
+  steps: [20, 30, 40, 50]
+  test_every_n_epochs: 5
+  
\ No newline at end of file
diff --git a/configs/STDEN/stde_zgc.yaml b/configs/STDEN/stde_zgc.yaml
new file mode 100644
index 0000000..f291538
--- /dev/null
+++ b/configs/STDEN/stde_zgc.yaml
@@ -0,0 +1,44 @@
+---
+log_base_dir: logs/BJ_XZ
+log_level: INFO
+
+data:
+  batch_size: 32
+  dataset_dir: data/BJ_XZ
+  val_batch_size: 32
+  graph_pkl_filename: data/sensor_graph/adj_XZ.npy
+
+model:
+  l1_decay: 0
+  seq_len: 12
+  horizon: 12
+  input_dim: 1
+  output_dim: 1
+  latent_dim: 4
+  n_traj_samples: 3
+  ode_method: dopri5
+  odeint_atol: 0.00001
+  odeint_rtol: 0.00001
+  rnn_units: 64
+  num_rnn_layers: 1
+  gcn_step: 2
+  filter_type: default # unkP IncP default
+  recg_type: gru
+  save_latent: false
+  nfe: false
+
+train:
+  base_lr: 0.01
+  dropout: 0
+  load: 0 # 0 for not load
+  epoch: 0 
+  epochs: 100
+  epsilon: 1.0e-3
+  lr_decay_ratio: 0.1
+  max_grad_norm: 5
+  min_learning_rate: 2.0e-06
+  optimizer: adam
+  patience: 20
+  steps: [20, 30, 40, 50]
+  test_every_n_epochs: 5
+  
\ No newline at end of file
diff --git a/models/STDEN/STDEN_modules.md b/models/STDEN/STDEN_modules.md
new file mode 100644
index 0000000..09cdc63
--- /dev/null
+++ b/models/STDEN/STDEN_modules.md
@@ -0,0 +1,88 @@
+### STDEN 模块与执行流（缩进层级表）
+
+模块 | 类/函数 | 输入 (shape) | 输出 (shape)
+--- | --- | --- | ---
+1 | STDENModel.forward | inputs: (seq_len, batch_size, num_edges x input_dim) | outputs: (horizon, batch_size, num_edges x output_dim); fe: (nfe:int, time:float)
+1.1 | Encoder_z0_RNN.forward | (seq_len, batch_size, num_edges x input_dim) | mean: (1, batch_size, num_nodes x latent_dim); std: (1, batch_size, num_nodes x latent_dim)
+1.1.1 | utils.sample_standard_gaussian | mu: (n_traj, batch, num_nodes x latent_dim); sigma: 同形状 | z0: (n_traj, batch, num_nodes x latent_dim)
+1.2 | DiffeqSolver.forward | first_point: (n_traj, batch, num_nodes x latent_dim); t: (horizon,) | sol_ys: (horizon, n_traj, batch, num_nodes x latent_dim); fe: (nfe:int, time:float)
+1.2.1 | ODEFunc.forward | t_local: 标量/1D; y: (B, num_nodes x latent_dim) | dy/dt: (B, num_nodes x latent_dim)
+1.3 | Decoder.forward | (horizon, n_traj, batch, num_nodes x latent_dim) | (horizon, batch, num_edges x output_dim)
+
+---
+
+### 细节模块 — Encoder_z0_RNN
+
+步骤 | 操作 | 输入 (shape) | 输出 (shape)
+--- | --- | --- | ---
+1 | 重塑到边批 | (seq_len, batch, num_edges x input_dim) | (seq_len, batch, num_edges, input_dim)
+2 | 合并边到批 | (seq_len, batch, num_edges, input_dim) | (seq_len, batch x num_edges, input_dim)
+3 | GRU 序列编码 | 同上 | (seq_len, batch x num_edges, rnn_units)
+4 | 取最后时间步 | 同上 | (batch x num_edges, rnn_units)
+5 | 还原边维 | (batch x num_edges, rnn_units) | (batch, num_edges, rnn_units)
+6 | 转置 + 边→节点映射 | (batch, num_edges, rnn_units) 经 inv_grad | (batch, num_nodes, rnn_units)
+7 | 全连接映射到 2x latent | (batch, num_nodes, rnn_units) | (batch, num_nodes, 2 x latent_dim)
+8 | 拆分均值/标准差 | 同上 | mean/std: (batch, num_nodes, latent_dim)
+9 | 展平并加时间维 | (batch, num_nodes, latent_dim) | (1, batch, num_nodes x latent_dim)
+
+备注：inv_grad 来源于 `utils.graph_grad(adj).T` 并做缩放；`hiddens_to_z0` 为两层 MLP + Tanh 后线性映射至 2 x latent_dim。
+
+---
+
+### 细节模块 — 采样（utils.sample_standard_gaussian）
+
+步骤 | 操作 | 输入 (shape) | 输出 (shape)
+--- | --- | --- | ---
+1 | 重复到 n_traj | mean/std: (1, batch, N·Z) → 重复 | (n_traj, batch, N·Z)
+2 | 重参数化采样 | mu, sigma | z0: (n_traj, batch, N·Z)
+
+其中 N·Z = num_nodes x latent_dim。
+
+---
+
+### 细节模块 — DiffeqSolver（含 ODEFunc 调用）
+
+步骤 | 操作 | 输入 (shape) | 输出 (shape)
+--- | --- | --- | ---
+1 | 合并样本维度 | first_point: (n_traj, batch, N·Z) | (n_traj x batch, N·Z)
+2 | ODE 积分 | t: (horizon,), y0 | pred_y: (horizon, n_traj x batch, N·Z)
+3 | 还原维度 | 同上 | (horizon, n_traj, batch, N·Z)
+4 | 统计代价 | odefunc.nfe, elapsed_time | fe: (nfe:int, time:float)
+
+ODEFunc 默认（filter_type="default"）为扩散过程：随机游走支持 + 多阶图卷积门控。
+
+---
+
+### 细节模块 — ODEFunc（默认扩散过程）
+
+步骤 | 操作 | 输入 (shape) | 输出 (shape)
+--- | --- | --- | ---
+1 | 形状整理 | y: (B, N·Z) → (B, N, Z) | (B, N, Z)
+2 | 多阶图卷积 _gconv | (B, N, Z) | (B, N, Z') 按需设置 Z'（通常保持 Z）
+3 | 门控 θ | _gconv(..., output=latent_dim) → Sigmoid | θ: (B, N·Z)
+4 | 生成场 ode_func_net | 堆叠 _gconv + 激活 | f(y): (B, N·Z)
+5 | 右端梯度 | - θ ⊙ f(y) | dy/dt: (B, N·Z)
+
+说明：
+- 支撑矩阵来自 `utils.calculate_random_walk_matrix(adj)`（正向/反向）并构造稀疏 Chebyshev 递推的多阶通道。
+- 若 `filter_type="unkP"`，则使用 `create_net` 的全连接网络在节点域逐点计算梯度。
+
+---
+
+### 细节模块 — Decoder
+
+步骤 | 操作 | 输入 (shape) | 输出 (shape)
+--- | --- | --- | ---
+1 | 重塑到节点域 | (T, S, B, N·Z) → (T, S, B, N, Z) | (T, S, B, N, Z)
+2 | 节点→边映射 | 乘以 graph_grad (N, E) | (T, S, B, Z, E)
+3 | 轨迹与通道均值 | 对 S 和 Z 维做均值 | (T, B, E)
+4 | 展平到输出维 | 考虑 output_dim（通常为 1） | (T, B, E x output_dim)
+
+符号：T=horizon，S=n_traj_samples，N=num_nodes，E=num_edges，Z=latent_dim，B=batch。
+
+---
+
+### 备注与约定
+- 内部采用边展平后的时序输入：`(seq_len, batch, num_edges x input_dim)`。
+- 图算子：`utils.graph_grad(adj)` 形状 `(N, E)`；`utils.calculate_random_walk_matrix(adj)` 生成随机游走稀疏矩阵用于图卷积。
+- 关键超参数（由配置传入）：`latent_dim`, `rnn_units`, `gcn_step`, `n_traj_samples`, `ode_method`, `horizon`, `input_dim`, `output_dim`。
diff --git a/models/STDEN/diffeq_solver.py b/models/STDEN/diffeq_solver.py
new file mode 100644
index 0000000..dfd0b17
--- /dev/null
+++ b/models/STDEN/diffeq_solver.py
@@ -0,0 +1,49 @@
+import torch
+import torch.nn as nn
+import time 
+
+from torchdiffeq import odeint
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+class DiffeqSolver(nn.Module):
+    def __init__(self, odefunc, method, latent_dim, 
+            odeint_rtol = 1e-4, odeint_atol = 1e-5):
+        nn.Module.__init__(self)
+
+        self.ode_method = method
+        self.odefunc = odefunc
+        self.latent_dim = latent_dim
+
+        self.rtol = odeint_rtol
+        self.atol = odeint_atol
+
+    def forward(self, first_point, time_steps_to_pred):
+        """
+        Decoder the trajectory through the ODE Solver.
+
+        :param time_steps_to_pred: horizon
+        :param first_point: (n_traj_samples, batch_size, num_nodes * latent_dim)
+        :return: pred_y: # shape (horizon, n_traj_samples, batch_size, self.num_nodes * self.output_dim)
+        """
+        n_traj_samples, batch_size = first_point.size()[0], first_point.size()[1] 
+        first_point = first_point.reshape(n_traj_samples * batch_size, -1) # reduce the complexity by merging dimension
+        
+        # pred_y shape: (horizon, n_traj_samples * batch_size, num_nodes * latent_dim)
+        start_time = time.time()
+        self.odefunc.nfe = 0
+        pred_y = odeint(self.odefunc, 
+                            first_point, 
+                            time_steps_to_pred, 
+                            rtol=self.rtol, 
+                            atol=self.atol,
+                            method=self.ode_method)
+        time_fe = time.time() - start_time
+        
+        # pred_y shape: (horizon, n_traj_samples, batch_size, num_nodes * latent_dim)
+        pred_y = pred_y.reshape(pred_y.size()[0], n_traj_samples, batch_size, -1)
+        # assert(pred_y.size()[1] == n_traj_samples)
+        # assert(pred_y.size()[2] == batch_size)
+        
+        return pred_y, (self.odefunc.nfe, time_fe)
+        
\ No newline at end of file
diff --git a/models/STDEN/ode_func.py b/models/STDEN/ode_func.py
new file mode 100644
index 0000000..10456e3
--- /dev/null
+++ b/models/STDEN/ode_func.py
@@ -0,0 +1,165 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+from models.STDEN import utils
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+class LayerParams:
+    def __init__(self, rnn_network: nn.Module, layer_type: str):
+        self._rnn_network = rnn_network
+        self._params_dict = {}
+        self._biases_dict = {}
+        self._type = layer_type
+
+    def get_weights(self, shape):
+        if shape not in self._params_dict:
+            nn_param = nn.Parameter(torch.empty(*shape, device=device))
+            nn.init.xavier_normal_(nn_param)
+            self._params_dict[shape] = nn_param
+            self._rnn_network.register_parameter('{}_weight_{}'.format(self._type, str(shape)),
+                                                 nn_param)
+        return self._params_dict[shape]
+
+    def get_biases(self, length, bias_start=0.0):
+        if length not in self._biases_dict:
+            biases = nn.Parameter(torch.empty(length, device=device))
+            nn.init.constant_(biases, bias_start)
+            self._biases_dict[length] = biases
+            self._rnn_network.register_parameter('{}_biases_{}'.format(self._type, str(length)),
+                                                 biases)
+
+        return self._biases_dict[length]
+
+class ODEFunc(nn.Module):
+    def __init__(self, num_units, latent_dim, adj_mx, gcn_step, num_nodes, 
+                gen_layers=1, nonlinearity='tanh', filter_type="default"):
+        """
+        :param num_units: dimensionality of the hidden layers 
+        :param latent_dim: dimensionality used for ODE (input and output). Analog of a continous latent state
+        :param adj_mx:
+        :param gcn_step:
+        :param num_nodes:
+        :param gen_layers: hidden layers in each ode func. 
+        :param nonlinearity:
+        :param filter_type: default
+        :param use_gc_for_ru: whether to use Graph convolution to calculate the reset and update gates.
+        """
+        super(ODEFunc, self).__init__()
+        self._activation = torch.tanh if nonlinearity == 'tanh' else torch.relu
+        
+        self._num_nodes = num_nodes
+        self._num_units = num_units # hidden dimension
+        self._latent_dim = latent_dim
+        self._gen_layers = gen_layers
+        self.nfe = 0
+        
+        self._filter_type = filter_type
+        if(self._filter_type == "unkP"):
+            ode_func_net = utils.create_net(latent_dim, latent_dim, n_units=num_units)
+            utils.init_network_weights(ode_func_net)
+            self.gradient_net = ode_func_net
+        else:
+            self._gcn_step = gcn_step
+            self._gconv_params = LayerParams(self, 'gconv')
+            self._supports = []
+            supports = []
+            supports.append(utils.calculate_random_walk_matrix(adj_mx).T)
+            supports.append(utils.calculate_random_walk_matrix(adj_mx.T).T)
+            
+            for support in supports:
+                self._supports.append(self._build_sparse_matrix(support))
+    
+    @staticmethod
+    def _build_sparse_matrix(L):
+        L = L.tocoo()
+        indices = np.column_stack((L.row, L.col))
+        # this is to ensure row-major ordering to equal torch.sparse.sparse_reorder(L)
+        indices = indices[np.lexsort((indices[:, 0], indices[:, 1]))]
+        L = torch.sparse_coo_tensor(indices.T, L.data, L.shape, device=device)
+        return L
+    
+    def forward(self, t_local, y, backwards = False):
+        """
+		Perform one step in solving ODE. Given current data point y and current time point t_local, returns gradient dy/dt at this time point
+
+		t_local: current time point
+		y: value at the current time point, shape (B, num_nodes * latent_dim)
+
+        :return
+        - Output: A `2-D` tensor with shape `(B, num_nodes * latent_dim)`.
+        """
+        self.nfe += 1
+        grad = self.get_ode_gradient_nn(t_local, y)
+        if backwards:
+            grad = -grad
+        return grad
+    
+    def get_ode_gradient_nn(self, t_local, inputs):
+        if(self._filter_type == "unkP"):
+            grad = self._fc(inputs)
+        elif (self._filter_type == "IncP"):
+            grad = - self.ode_func_net(inputs)
+        else: # default is diffusion process
+            # theta shape: (B, num_nodes * latent_dim)
+            theta = torch.sigmoid(self._gconv(inputs, self._latent_dim, bias_start=1.0)) 
+            grad = - theta * self.ode_func_net(inputs)
+        return grad
+
+    def ode_func_net(self, inputs):
+        c = inputs
+        for i in range(self._gen_layers):
+            c = self._gconv(c, self._num_units)
+            c = self._activation(c)
+        c = self._gconv(c, self._latent_dim)
+        c = self._activation(c)
+        return c
+    
+    def _fc(self, inputs):
+        batch_size = inputs.size()[0]
+        grad = self.gradient_net(inputs.view(batch_size * self._num_nodes, self._latent_dim))
+        return grad.reshape(batch_size, self._num_nodes * self._latent_dim) # (batch_size, num_nodes, latent_dim)
+    
+    @staticmethod
+    def _concat(x, x_):
+        x_ = x_.unsqueeze(0)
+        return torch.cat([x, x_], dim=0)
+
+    def _gconv(self, inputs, output_size, bias_start=0.0):
+        # Reshape input and state to (batch_size, num_nodes, input_dim/state_dim)
+        batch_size = inputs.shape[0]
+        inputs = torch.reshape(inputs, (batch_size, self._num_nodes, -1))
+        # state = torch.reshape(state, (batch_size, self._num_nodes, -1))
+        # inputs_and_state = torch.cat([inputs, state], dim=2)
+        input_size = inputs.size(2)
+
+        x = inputs
+        x0 = x.permute(1, 2, 0)  # (num_nodes, total_arg_size, batch_size)
+        x0 = torch.reshape(x0, shape=[self._num_nodes, input_size * batch_size])
+        x = torch.unsqueeze(x0, 0)
+
+        if self._gcn_step == 0:
+            pass
+        else:
+            for support in self._supports:
+                x1 = torch.sparse.mm(support, x0)
+                x = self._concat(x, x1)
+
+                for k in range(2, self._gcn_step + 1):
+                    x2 = 2 * torch.sparse.mm(support, x1) - x0
+                    x = self._concat(x, x2)
+                    x1, x0 = x2, x1
+
+        num_matrices = len(self._supports) * self._gcn_step + 1  # Adds for x itself.
+        x = torch.reshape(x, shape=[num_matrices, self._num_nodes, input_size, batch_size])
+        x = x.permute(3, 1, 2, 0)  # (batch_size, num_nodes, input_size, order)
+        x = torch.reshape(x, shape=[batch_size * self._num_nodes, input_size * num_matrices])
+
+        weights = self._gconv_params.get_weights((input_size * num_matrices, output_size))
+        x = torch.matmul(x, weights)  # (batch_size * self._num_nodes, output_size)
+
+        biases = self._gconv_params.get_biases(output_size, bias_start)
+        x += biases
+        # Reshape res back to 2D: (batch_size, num_node, state_dim) -> (batch_size, num_node * state_dim)
+        return torch.reshape(x, [batch_size, self._num_nodes * output_size])
diff --git a/models/STDEN/stden_model.py b/models/STDEN/stden_model.py
new file mode 100644
index 0000000..0b6ae5f
--- /dev/null
+++ b/models/STDEN/stden_model.py
@@ -0,0 +1,181 @@
+import torch
+import torch.nn as nn
+from torch.nn.modules.rnn import GRU
+from models.STDEN.ode_func import ODEFunc
+from models.STDEN.diffeq_solver import DiffeqSolver
+from models.STDEN import utils
+from data.graph_loader import load_graph
+
+class EncoderAttrs:
+    """编码器属性配置类"""
+    def __init__(self, config, adj_mx):
+        self.adj_mx = adj_mx
+        self.num_nodes = adj_mx.shape[0]
+        self.num_edges = (adj_mx > 0.).sum()
+        self.gcn_step = int(config.get('gcn_step', 2))
+        self.filter_type = config.get('filter_type', 'default')
+        self.num_rnn_layers = int(config.get('num_rnn_layers', 1))
+        self.rnn_units = int(config.get('rnn_units'))
+        self.latent_dim = int(config.get('latent_dim', 4))
+
+
+class STDENModel(nn.Module, EncoderAttrs):
+    """STDEN主模型：时空微分方程网络"""
+    def __init__(self, config):
+        nn.Module.__init__(self)
+        adj_mx = load_graph(config)
+        EncoderAttrs.__init__(self, config['model'], adj_mx)
+
+        # 识别网络
+        self.encoder_z0 = Encoder_z0_RNN(config['model'], adj_mx)
+
+        model_kwargs = config['model']
+        # ODE求解器配置
+        self.n_traj_samples = int(model_kwargs.get('n_traj_samples', 1))
+        self.ode_method = model_kwargs.get('ode_method', 'dopri5')
+        self.atol = float(model_kwargs.get('odeint_atol', 1e-4))
+        self.rtol = float(model_kwargs.get('odeint_rtol', 1e-3))
+        self.num_gen_layer = int(model_kwargs.get('gen_layers', 1))
+        self.ode_gen_dim = int(model_kwargs.get('gen_dim', 64))
+        
+        # 创建ODE函数和求解器
+        odefunc = ODEFunc(
+            self.ode_gen_dim, self.latent_dim, adj_mx, 
+            self.gcn_step, self.num_nodes, filter_type=self.filter_type
+        )
+        
+        self.diffeq_solver = DiffeqSolver(
+            odefunc, self.ode_method, self.latent_dim,
+            odeint_rtol=self.rtol, odeint_atol=self.atol
+        )
+
+        # 潜在特征保存设置
+        self.save_latent = bool(model_kwargs.get('save_latent', False))
+        self.latent_feat = None
+
+        # 解码器
+        self.horizon = int(model_kwargs.get('horizon', 1))
+        self.out_feat = int(model_kwargs.get('output_dim', 1))
+        self.decoder = Decoder(
+            self.out_feat, adj_mx, self.num_nodes, self.num_edges
+        )
+
+    def forward(self, inputs, labels=None, batches_seen=None):
+        """
+        seq2seq前向传播
+        :param inputs: (seq_len, batch_size, num_edges * input_dim)
+        :param labels: (horizon, batch_size, num_edges * output_dim)
+        :param batches_seen: 已见批次数量
+        :return: outputs: (horizon, batch_size, num_edges * output_dim)
+        """
+        # 编码初始潜在状态
+        B, T, N, C = inputs.shape
+        inputs = inputs.view(T, B, N * C)
+        first_point_mu, first_point_std = self.encoder_z0(inputs)
+
+        # 采样轨迹
+        means_z0 = first_point_mu.repeat(self.n_traj_samples, 1, 1)
+        sigma_z0 = first_point_std.repeat(self.n_traj_samples, 1, 1)
+        first_point_enc = utils.sample_standard_gaussian(means_z0, sigma_z0)
+
+        # 时间步预测
+        time_steps_to_predict = torch.arange(start=0, end=self.horizon, step=1).float()
+        time_steps_to_predict = time_steps_to_predict / len(time_steps_to_predict)
+
+        # ODE求解
+        sol_ys, fe = self.diffeq_solver(first_point_enc, time_steps_to_predict)
+        
+        if self.save_latent:
+            self.latent_feat = torch.mean(sol_ys.detach(), axis=1)
+        # 解码输出
+        outputs = self.decoder(sol_ys)
+
+        outputs = outputs.view(B, T, N, C)
+
+        return outputs, fe
+
+
+class Encoder_z0_RNN(nn.Module, EncoderAttrs):
+    """RNN编码器：将输入序列编码为初始潜在状态"""
+    def __init__(self, config, adj_mx):
+        nn.Module.__init__(self)
+        EncoderAttrs.__init__(self, config, adj_mx)
+        
+        self.recg_type = config.get('recg_type', 'gru')
+        self.input_dim = int(config.get('input_dim', 1))
+        
+        if self.recg_type == 'gru':
+            self.gru_rnn = GRU(self.input_dim, self.rnn_units)
+        else:
+            raise NotImplementedError("只支持'gru'识别网络")
+
+        # 隐藏状态到z0的映射
+        self.inv_grad = utils.graph_grad(adj_mx).transpose(-2, -1)
+        self.inv_grad[self.inv_grad != 0.] = 0.5
+        
+        self.hiddens_to_z0 = nn.Sequential(
+            nn.Linear(self.rnn_units, 50),
+            nn.Tanh(),
+            nn.Linear(50, self.latent_dim * 2)
+        )
+        utils.init_network_weights(self.hiddens_to_z0)
+
+    def forward(self, inputs):
+        """
+        编码器前向传播
+        :param inputs: (seq_len, batch_size, num_edges * input_dim)
+        :return: mean, std: (1, batch_size, latent_dim)
+        """
+        seq_len, batch_size = inputs.size(0), inputs.size(1)
+        
+        # 重塑输入并处理
+        inputs = inputs.reshape(seq_len, batch_size, self.num_nodes, self.input_dim)
+        inputs = inputs.reshape(seq_len, batch_size * self.num_nodes, self.input_dim)
+        
+        # GRU处理
+        outputs, _ = self.gru_rnn(inputs)
+        last_output = outputs[-1]
+        
+        # 重塑并转换维度
+        last_output = torch.reshape(last_output, (batch_size, self.num_nodes, -1))
+        last_output = torch.transpose(last_output, (-2, -1))
+        last_output = torch.matmul(last_output, self.inv_grad).transpose(-2, -1)
+        
+        # 生成均值和标准差
+        mean, std = utils.split_last_dim(self.hiddens_to_z0(last_output))
+        mean = mean.reshape(batch_size, -1)
+        std = std.reshape(batch_size, -1).abs()
+
+        return mean.unsqueeze(0), std.unsqueeze(0)
+
+
+class Decoder(nn.Module):
+    """解码器：将潜在状态解码为输出"""
+    def __init__(self, output_dim, adj_mx, num_nodes, num_edges):
+        super(Decoder, self).__init__()
+        self.num_nodes = num_nodes
+        self.num_edges = num_edges
+        self.grap_grad = utils.graph_grad(adj_mx)
+        self.output_dim = output_dim
+    
+    def forward(self, inputs):
+        """
+        :param inputs: (horizon, n_traj_samples, batch_size, num_nodes * latent_dim)
+        :return: outputs: (horizon, batch_size, num_edges * output_dim)
+        """
+        horizon, n_traj_samples, batch_size = inputs.size()[:3]
+        
+        # 重塑输入
+        inputs = inputs.reshape(horizon, n_traj_samples, batch_size, self.num_nodes, -1).transpose(-2, -1)
+        latent_dim = inputs.size(-2)
+        
+        # 图梯度变换：从节点到边
+        outputs = torch.matmul(inputs, self.grap_grad)
+        
+        # 重塑并平均采样轨迹
+        outputs = outputs.reshape(horizon, n_traj_samples, batch_size, latent_dim, self.num_nodes, self.output_dim)
+        outputs = torch.mean(torch.mean(outputs, axis=3), axis=1)
+        outputs = outputs.reshape(horizon, batch_size, -1)
+        
+        return outputs
+
diff --git a/models/STDEN/utils.py b/models/STDEN/utils.py
new file mode 100644
index 0000000..a17e9cc
--- /dev/null
+++ b/models/STDEN/utils.py
@@ -0,0 +1,234 @@
+import logging
+import numpy as np
+import os
+import time
+import scipy.sparse as sp
+import sys
+import torch
+import torch.nn as nn
+
+
+class DataLoader(object):
+    def __init__(self, xs, ys, batch_size, pad_with_last_sample=True, shuffle=False):
+        """
+
+        :param xs:
+        :param ys:
+        :param batch_size:
+        :param pad_with_last_sample: pad with the last sample to make number of samples divisible to batch_size.
+        """
+        self.batch_size = batch_size
+        self.current_ind = 0
+        if pad_with_last_sample:
+            num_padding = (batch_size - (len(xs) % batch_size)) % batch_size
+            x_padding = np.repeat(xs[-1:], num_padding, axis=0)
+            y_padding = np.repeat(ys[-1:], num_padding, axis=0)
+            xs = np.concatenate([xs, x_padding], axis=0)
+            ys = np.concatenate([ys, y_padding], axis=0)
+        self.size = len(xs)
+        self.num_batch = int(self.size // self.batch_size)
+        if shuffle:
+            permutation = np.random.permutation(self.size)
+            xs, ys = xs[permutation], ys[permutation]
+        self.xs = xs
+        self.ys = ys
+
+    def get_iterator(self):
+        self.current_ind = 0
+
+        def _wrapper():
+            while self.current_ind < self.num_batch:
+                start_ind = self.batch_size * self.current_ind
+                end_ind = min(self.size, self.batch_size * (self.current_ind + 1))
+                x_i = self.xs[start_ind: end_ind, ...]
+                y_i = self.ys[start_ind: end_ind, ...]
+                yield (x_i, y_i)
+                self.current_ind += 1
+
+        return _wrapper()
+
+
+class StandardScaler:
+    """
+    Standard the input
+    """
+
+    def __init__(self, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def transform(self, data):
+        return (data - self.mean) / self.std
+
+    def inverse_transform(self, data):
+        return (data * self.std) + self.mean
+
+
+def calculate_random_walk_matrix(adj_mx):
+    adj_mx = sp.coo_matrix(adj_mx)
+    d = np.array(adj_mx.sum(1))
+    d_inv = np.power(d, -1).flatten()
+    d_inv[np.isinf(d_inv)] = 0.
+    d_mat_inv = sp.diags(d_inv)
+    random_walk_mx = d_mat_inv.dot(adj_mx).tocoo()
+    return random_walk_mx
+
+
+def config_logging(log_dir, log_filename='info.log', level=logging.INFO):
+    # Add file handler and stdout handler
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    # Create the log directory if necessary.
+    try:
+        os.makedirs(log_dir)
+    except OSError:
+        pass
+    file_handler = logging.FileHandler(os.path.join(log_dir, log_filename))
+    file_handler.setFormatter(formatter)
+    file_handler.setLevel(level=level)
+    # Add console handler.
+    console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setFormatter(console_formatter)
+    console_handler.setLevel(level=level)
+    logging.basicConfig(handlers=[file_handler, console_handler], level=level)
+
+
+def get_logger(log_dir, name, log_filename='info.log', level=logging.INFO):
+    logger = logging.getLogger(name)
+    logger.setLevel(level)
+    # Add file handler and stdout handler
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    file_handler = logging.FileHandler(os.path.join(log_dir, log_filename))
+    file_handler.setFormatter(formatter)
+    # Add console handler.
+    console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setFormatter(console_formatter)
+    logger.addHandler(file_handler)
+    logger.addHandler(console_handler)
+    # Add google cloud log handler
+    logger.info('Log directory: %s', log_dir)
+    return logger
+
+
+def get_log_dir(kwargs):
+    log_dir = kwargs['train'].get('log_dir')
+    if log_dir is None:
+        batch_size = kwargs['data'].get('batch_size')
+
+        filter_type = kwargs['model'].get('filter_type')
+        gcn_step = kwargs['model'].get('gcn_step')
+        horizon = kwargs['model'].get('horizon')
+        latent_dim = kwargs['model'].get('latent_dim')
+        n_traj_samples = kwargs['model'].get('n_traj_samples')
+        ode_method = kwargs['model'].get('ode_method')
+
+        seq_len = kwargs['model'].get('seq_len')
+        rnn_units = kwargs['model'].get('rnn_units')
+        recg_type = kwargs['model'].get('recg_type')
+
+        if filter_type == 'unkP':
+            filter_type_abbr = 'UP'
+        elif filter_type == 'IncP':
+            filter_type_abbr = 'NV'
+        else:
+            filter_type_abbr = 'DF'
+
+        run_id = 'STDEN_%s-%d_%s-%d_L-%d_N-%d_M-%s_bs-%d_%d-%d_%s/' % (
+            recg_type, rnn_units, filter_type_abbr, gcn_step, latent_dim, n_traj_samples, ode_method, batch_size,
+            seq_len, horizon, time.strftime('%m%d%H%M%S'))
+        base_dir = kwargs.get('log_base_dir')
+        log_dir = os.path.join(base_dir, run_id)
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    return log_dir
+
+
+def load_dataset(dataset_dir, batch_size, val_batch_size=None, **kwargs):
+    if ('BJ' in dataset_dir):
+        data = dict(np.load(os.path.join(dataset_dir, 'flow.npz')))  # convert readonly NpzFile to writable dict Object
+        for category in ['train', 'val', 'test']:
+            data['x_' + category] = data['x_' + category]  # [..., :4] # ignore the time index
+    else:
+        data = {}
+        for category in ['train', 'val', 'test']:
+            cat_data = np.load(os.path.join(dataset_dir, category + '.npz'))
+            data['x_' + category] = cat_data['x']
+            data['y_' + category] = cat_data['y']
+    scaler = StandardScaler(mean=data['x_train'].mean(), std=data['x_train'].std())
+    # Data format
+    for category in ['train', 'val', 'test']:
+        data['x_' + category] = scaler.transform(data['x_' + category])
+        data['y_' + category] = scaler.transform(data['y_' + category])
+    data['train_loader'] = DataLoader(data['x_train'], data['y_train'], batch_size, shuffle=True)
+    data['val_loader'] = DataLoader(data['x_val'], data['y_val'], val_batch_size, shuffle=False)
+    data['test_loader'] = DataLoader(data['x_test'], data['y_test'], val_batch_size, shuffle=False)
+    data['scaler'] = scaler
+
+    return data
+
+
+def load_graph_data(pkl_filename):
+    adj_mx = np.load(pkl_filename)
+    return adj_mx
+
+
+def graph_grad(adj_mx):
+    """Fetch the graph gradient operator."""
+    num_nodes = adj_mx.shape[0]
+
+    num_edges = (adj_mx > 0.).sum()
+    grad = torch.zeros(num_nodes, num_edges)
+    e = 0
+    for i in range(num_nodes):
+        for j in range(num_nodes):
+            if adj_mx[i, j] == 0:
+                continue
+            grad[i, e] = 1.
+            grad[j, e] = -1.
+            e += 1
+    return grad
+
+
+def init_network_weights(net, std=0.1):
+    """
+    Just for nn.Linear net.
+    """
+    for m in net.modules():
+        if isinstance(m, nn.Linear):
+            nn.init.normal_(m.weight, mean=0, std=std)
+            nn.init.constant_(m.bias, val=0)
+
+
+def split_last_dim(data):
+    last_dim = data.size()[-1]
+    last_dim = last_dim // 2
+
+    res = data[..., :last_dim], data[..., last_dim:]
+    return res
+
+
+def get_device(tensor):
+    device = torch.device("cpu")
+    if tensor.is_cuda:
+        device = tensor.get_device()
+    return device
+
+
+def sample_standard_gaussian(mu, sigma):
+    device = get_device(mu)
+
+    d = torch.distributions.normal.Normal(torch.Tensor([0.]).to(device), torch.Tensor([1.]).to(device))
+    r = d.sample(mu.size()).squeeze(-1)
+    return r * sigma.float() + mu.float()
+
+
+def create_net(n_inputs, n_outputs, n_layers=0, n_units=100, nonlinear=nn.Tanh):
+    layers = [nn.Linear(n_inputs, n_units)]
+    for i in range(n_layers):
+        layers.append(nonlinear())
+        layers.append(nn.Linear(n_units, n_units))
+
+    layers.append(nonlinear())
+    layers.append(nn.Linear(n_units, n_outputs))
+    return nn.Sequential(*layers)