Agent Conversation: google/gemini-3.1-pro-preview
Model: google/gemini-3.1-pro-preview
Total steps: 6
Messages: 13
UserStep 0
Task: ts-exogenous-forecast
Exogenous Variable Forecasting: Custom Model Design
Objective
Design and implement a custom deep learning model for time series forecasting with exogenous (external) variables. Uses features=MS: all variables as input, predict only the target (last dimension). Your code goes in the Model class in models/Custom.py. Three reference implementations (DLinear, PatchTST, iTransformer) are provided as read-only.
Evaluation
Trained and evaluated on three datasets with MS features:
- ETTh1 (7 → 1, hourly electricity data)
- Weather (21 → 1, weather observations)
- ECL (321 → 1, electricity consumption)
All use seq_len=96, pred_len=96. Metrics: MSE and MAE on the target variable (lower is better). The framework automatically extracts outputs[:, :, -1:].
Time-Series-Library/models/Custom.py [EDITABLE — entire file only]
1: import torch
2: import torch.nn as nn
3:
4:
5: class Model(nn.Module):
6: """
7: Custom model for exogenous variable forecasting (features=MS).
8:
9: Forward signature: forward(x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None)
10: - x_enc: [batch, seq_len, enc_in] — all input variables
11: - x_mark_enc: [batch, seq_len, time_features] — time feature encoding
12: - x_dec: [batch, label_len+pred_len, dec_in] — decoder input
13: - x_mark_dec: [batch, label_len+pred_len, time_features] — decoder time features
14:
15: Must return: [batch, pred_len, c_out] for forecasting
16: Note: c_out = enc_in. The framework extracts the target (last dim) for MS mode.
17: """
18:
19: def __init__(self, configs):
20: super(Model, self).__init__()
21: self.task_name = configs.task_name
22: self.seq_len = configs.seq_len
23: self.pred_len = configs.pred_len
24: self.enc_in = configs.enc_in
25: self.c_out = configs.c_out
26: # TODO: Define your model architecture here
27:
28: def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
29: """
30: Forecasting with exogenous variables.
31: Input: x_enc [batch, seq_len, enc_in] — all variables
32: Output: [batch, pred_len, c_out] — predict all variables
33: """
34: # TODO: Implement your forecasting logic
35: batch_size = x_enc.shape[0]
36: return torch.zeros(batch_size, self.pred_len, self.c_out).to(x_enc.device)
37:
38: def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
39: if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
40: dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
41: return dec_out[:, -self.pred_len:, :]
42: return NoneTime-Series-Library/models/DLinear.py [REA
AssistantStep 1edit
Tool Input
1{
2 "start_line": 2,
3 "content": "import torch.nn as nn\nimport torch.nn.functional as F\n\nclass moving_avg(nn.Module):\n def __init__(self, kernel_size, stride):\n super(moving_avg, self).__init__()\n self.kernel_size = kernel_size\n self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)\n\n def forward(self, x):\n front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)\n end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)\n x = torch.cat([front, x, end], dim=1)\n x = self.avg(x.permute(0, 2, 1))\n x = x.permute(0, 2, 1)\n return x\n\nclass series_decomp(nn.Module):\n def __init__(self, kernel_size):\n super(series_decomp, self).__init__()\n self.moving_avg = moving_avg(kernel_size, stride=1)\n\n def forward(self, x):\n moving_mean = self.moving_avg(x)\n res = x - moving_mean\n return res, moving_mean\n\nclass TSMixerBlock(nn.Module):\n def __init__(self, seq_len, num_vars, ff_dim, dropout=0.1):\n super().__init__()\n self.norm1 = nn.LayerNorm(num_vars)\n self.lin_time1 = nn.Linear(seq_len, ff_dim)\n self.lin_time2 = nn.Linear(ff_dim, seq_len)\n \n self.norm2 = nn.LayerNorm(num_vars)\n self.lin_var1 = nn.Linear(num_vars, ff_dim)\n self.lin_var2 = nn.Linear(ff_dim, num_vars)\n \n self.dropout = nn.Dropout(dropout)\n \n def forward(self, x):\n # x: [B, S, C]\n res = x\n x = self.norm1(x)\n x = x.transpose(1, 2)\n x = F.gelu(self.lin_time1(x))\n x = self.dropout(x)\n x = self.lin_time2(x)\n x = self.dropout(x)\n x = x.transpose(1, 2)\n x = x + res\n \n res = x\n x = self.norm2(x)\n x = F.gelu(self.lin_var1(x))\n x = self.dropout(x)\n x = self.lin_var2(x)\n x = self.dropout(x)\n x = x + res\n \n return x\n\nclass Model(nn.Module):\n def __init__(self, configs):\n super(Model, self).__init__()\n self.task_name = configs.task_name\n self.seq_len = configs.seq_len\n self.pred_len = configs.pred_len\n self.enc_in = configs.enc_in\n self.c_out = configs.c_out\n \n self.decomp = series_decomp(kernel_size=25)\n \n self.num_blocks = 2\n d_model = getattr(configs, 'd_model', 256)\n dropout = getattr(configs, 'dropout', 0.1)\n \n self.seasonal_blocks = nn.ModuleList([\n TSMixerBlock(self.seq_len, self.enc_in, ff_dim=d_model, dropout=dropout) \n for _ in range(self.num_blocks)\n ])\n \n self.trend_lin = nn.Linear(self.seq_len, self.pred_len)\n self.seasonal_lin = nn.Linear(self.seq_len, self.pred_len)\n \n def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):\n means = x_enc.mean(1, keepdim=True).detach()\n x_enc = x_enc - means\n stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)\n x_enc /= stdev\n \n seasonal, trend = self.decomp(x_enc)\n \n trend = self.trend_lin(trend.transpose(1, 2)).transpose(1, 2)\n \n for block in self.seasonal_blocks:\n seasonal = block(seasonal)\n seasonal = self.seasonal_lin(seasonal.transpose(1, 2)).transpose(1, 2)\n \n dec_out = seasonal + trend\n \n dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))\n dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))\n \n return dec_out\n\n def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):\n if self.task_name in ['long_term_forecast', 'short_term_forecast']:\n dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)\n return dec_out[:, -self.pred_len:, :]\n return None",
4 "after_line": 1,
5 "op": "replace",
6 "end_line": 42,
7 "filename": "Time-Series-Library/models/Custom.py"
8}Tool ResultStep 1
OK: Replaced lines 2..42 with 111 line(s) in Time-Series-Library/models/Custom.py. Editable range: entire file.
[Current file: Time-Series-Library/models/Custom.py | editable: 1–112 | total: 112 lines]
1 import torch
2 import torch.nn as nn
3 import torch.nn.functional as F
4
5 class moving_avg(nn.Module):
6 def __init__(self, kernel_size, stride):
7 super(moving_avg, self).__init__()
8 self.kernel_size = kernel_size
9 self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)
10
11 def forward(self, x):
12 front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)
13 end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)
14 x = torch.cat([front, x, end], dim=1)
15 x = self.avg(x.permute(0, 2, 1))
16 x = x.permute(0, 2, 1)
17 return x
18
19 class series_decomp(nn.Module):
AssistantStep 2test
Tool ResultStep 2
[Test #1] (2 tests remaining; call submit(n=1) to submit this result) ### ETTh1 (scripts/etth1.sh) Training metrics (last epochs): TRAIN_METRICS epoch=2 train_loss=0.1723574 vali_loss=0.1060688 test_loss=0.0619710 TRAIN_METRICS epoch=3 train_loss=0.1613503 vali_loss=0.1050599 test_loss=0.0625452 TRAIN_METRICS epoch=4 train_loss=0.1568993 vali_loss=0.1105298 test_loss=0.0646573 TRAIN_METRICS epoch=5 train_loss=0.1548016 vali_loss=0.1083567 test_loss=0.0645598 TRAIN_METRICS epoch=6 train_loss=0.1543518 vali_loss=0.1066642 test_loss=0.0640065 Test results (ETTh1): MSE: 0.061699, MAE: 0.188542 ### Weather (scripts/weather.sh) Training metrics (last epochs): TRAIN_METRICS epoch=2 train_loss=0.9673052 vali_loss=0.0034412 test_loss=0.0013427 TRAIN_METRICS epoch=3 train_loss=0.9277234 vali_loss=0.0033905 test_loss=0.0013219 TRAIN_METRICS epoch=4 train_loss=0.9162266 vali_loss=0.0034488 test_loss=0.0013405 TRAIN_METRICS epoch=5 train_loss=0.9125708 vali_loss=0.0034299 test_loss=0.0013329 TR
AssistantStep 3edit
Tool Input
1{
2 "filename": "Time-Series-Library/models/Custom.py",
3 "content": "import torch.nn as nn\nimport torch.nn.functional as F\n\nclass TokExoMixerBlock(nn.Module):\n def __init__(self, P, C, d_model, dropout=0.1):\n super().__init__()\n self.norm1 = nn.LayerNorm(d_model)\n self.time_mlp = nn.Sequential(\n nn.Linear(P, d_model),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(d_model, P),\n nn.Dropout(dropout)\n )\n \n self.norm2 = nn.LayerNorm(d_model)\n self.var_mlp = nn.Sequential(\n nn.Linear(C, d_model),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(d_model, C),\n nn.Dropout(dropout)\n )\n \n self.norm3 = nn.LayerNorm(d_model)\n self.channel_mlp = nn.Sequential(\n nn.Linear(d_model, d_model * 2),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(d_model * 2, d_model),\n nn.Dropout(dropout)\n )\n\n def forward(self, x):\n # x: [B, C, P, d_model]\n res = x\n x1 = self.norm1(x).transpose(2, 3)\n x1 = self.time_mlp(x1).transpose(2, 3)\n x = res + x1\n \n res = x\n x2 = self.norm2(x).permute(0, 2, 3, 1)\n x2 = self.var_mlp(x2).permute(0, 3, 1, 2)\n x = res + x2\n \n res = x\n x3 = self.channel_mlp(self.norm3(x))\n x = res + x3\n \n return x\n\nclass Model(nn.Module):\n def __init__(self, configs):\n super(Model, self).__init__()\n self.task_name = configs.task_name\n self.seq_len = configs.seq_len\n self.pred_len = configs.pred_len\n self.enc_in = configs.enc_in\n self.c_out = configs.c_out\n \n self.patch_len = 16\n self.stride = 8\n self.padding = self.stride\n \n self.patch_num = int((self.seq_len - self.patch_len) / self.stride + 2)\n self.d_model = getattr(configs, 'd_model', 256)\n dropout = getattr(configs, 'dropout', 0.1)\n \n self.padding_patch_layer = nn.ReplicationPad1d((0, self.padding))\n self.value_embedding = nn.Linear(self.patch_len, self.d_model, bias=False)\n self.position_embedding = nn.Parameter(torch.randn(self.patch_num, self.d_model))\n \n self.num_blocks = 3\n self.blocks = nn.ModuleList([\n TokExoMixerBlock(self.patch_num, self.enc_in, self.d_model, dropout=dropout)\n for _ in range(self.num_blocks)\n ])\n \n self.head_nf = self.d_model * self.patch_num\n self.head = nn.Sequential(\n nn.Flatten(start_dim=-2),\n nn.Linear(self.head_nf, self.pred_len)\n )\n \n def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):\n means = x_enc.mean(1, keepdim=True).detach()\n x_enc = x_enc - means\n stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)\n x_enc /= stdev\n \n x_enc = x_enc.transpose(1, 2) # [B, C, S]\n x_enc = self.padding_patch_layer(x_enc)\n x_enc = x_enc.unfold(dimension=-1, size=self.patch_len, step=self.stride)\n \n x_enc = self.value_embedding(x_enc)\n x_enc = x_enc + self.position_embedding\n \n for block in self.blocks:\n x_enc = block(x_enc)\n \n dec_out = self.head(x_enc)\n dec_out = dec_out.transpose(1, 2)\n \n dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))\n dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))\n \n return dec_out\n\n def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):\n if self.task_name in ['long_term_forecast', 'short_term_forecast']:\n dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)\n return dec_out[:, -self.pred_len:, :]\n return None",
4 "start_line": 2,
5 "end_line": 112,
6 "after_line": 1,
7 "op": "replace"
8}Tool ResultStep 3
OK: Replaced lines 2..112 with 113 line(s) in Time-Series-Library/models/Custom.py. Editable range: entire file.
[Current file: Time-Series-Library/models/Custom.py | editable: 1–114 | total: 114 lines]
1 import torch
2 import torch.nn as nn
3 import torch.nn.functional as F
4
5 class TokExoMixerBlock(nn.Module):
6 def __init__(self, P, C, d_model, dropout=0.1):
7 super().__init__()
8 self.norm1 = nn.LayerNorm(d_model)
9 self.time_mlp = nn.Sequential(
10 nn.Linear(P, d_model),
11 nn.GELU(),
12 nn.Dropout(dropout),
13 nn.Linear(d_model, P),
14 nn.Dropout(dropout)
15 )
16
17 self.norm2 = nn.LayerNorm(d_model)
18 self.var_mlp = nn.Sequential(
19 nn.Linear(C, d_model),
20 nn.GELU(),
21 nn.Dropout(dropout),
22
AssistantStep 4test
Tool ResultStep 4
[Test #2] (1 test remaining; call submit(n=2) to submit this result) ### ETTh1 (scripts/etth1.sh) Training metrics (last epochs): TRAIN_METRICS epoch=1 train_loss=0.1688143 vali_loss=0.1256526 test_loss=0.0639472 TRAIN_METRICS epoch=2 train_loss=0.1523415 vali_loss=0.1319201 test_loss=0.0607083 TRAIN_METRICS epoch=3 train_loss=0.1405581 vali_loss=0.1845603 test_loss=0.0642450 TRAIN_METRICS epoch=4 train_loss=0.1304743 vali_loss=0.1813623 test_loss=0.0615638 Test results (ETTh1): MSE: 0.063555, MAE: 0.187960 ### Weather (scripts/weather.sh) Training metrics (last epochs): TRAIN_METRICS epoch=3 train_loss=0.8987718 vali_loss=0.0034069 test_loss=0.0013776 TRAIN_METRICS epoch=4 train_loss=0.8878495 vali_loss=0.0032110 test_loss=0.0013307 TRAIN_METRICS epoch=5 train_loss=0.8873496 vali_loss=0.0033617 test_loss=0.0013564 TRAIN_METRICS epoch=6 train_loss=0.8841517 vali_loss=0.0033181 test_loss=0.0013497 TRAIN_METRICS epoch=7 train_loss=0.8808482 vali_loss=0.0033169 test_loss=0.0013510 Tes
AssistantStep 5edit
Tool Input
1{
2 "end_line": 114,
3 "start_line": 2,
4 "after_line": 1,
5 "content": "import torch.nn as nn\nimport torch.nn.functional as F\nimport math\n\nclass Model(nn.Module):\n \"\"\"\n Combined Patch-Time and Variable-Attention Model for Exogenous Forecasting\n 1. Patching sequences and applying Transformer over Time (Patches)\n 2. Flattening temporal representations and applying Self-Attention over Variables\n \"\"\"\n def __init__(self, configs):\n super(Model, self).__init__()\n self.task_name = configs.task_name\n self.seq_len = configs.seq_len\n self.pred_len = configs.pred_len\n self.enc_in = configs.enc_in\n self.c_out = configs.c_out\n \n self.patch_len = 16\n self.stride = 8\n self.padding = self.stride\n \n self.patch_num = int((self.seq_len - self.patch_len) / self.stride + 2)\n \n # d_model for patches\n self.d_model = getattr(configs, 'd_model', 128)\n if self.d_model > 256:\n self.d_model = 128\n \n dropout = getattr(configs, 'dropout', 0.1)\n \n self.patch_emb = nn.Linear(self.patch_len, self.d_model)\n self.pos_emb = nn.Parameter(torch.randn(self.patch_num, self.d_model))\n \n # Time Encoder\n encoder_layer = nn.TransformerEncoderLayer(\n d_model=self.d_model, \n nhead=4, \n dim_feedforward=self.d_model * 4, \n dropout=dropout, \n batch_first=True,\n activation='gelu'\n )\n self.time_encoder = nn.TransformerEncoder(encoder_layer, num_layers=2)\n \n # Variable Attention\n self.embed_dim_var = self.patch_num * self.d_model\n \n self.var_attn = nn.MultiheadAttention(\n embed_dim=self.embed_dim_var, \n num_heads=4, \n dropout=dropout, \n batch_first=True\n )\n self.norm_var = nn.LayerNorm(self.embed_dim_var)\n self.norm_var_out = nn.LayerNorm(self.embed_dim_var)\n \n self.var_ffn = nn.Sequential(\n nn.Linear(self.embed_dim_var, self.embed_dim_var),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(self.embed_dim_var, self.embed_dim_var),\n nn.Dropout(dropout)\n )\n \n self.head = nn.Linear(self.embed_dim_var, self.pred_len)\n\n def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):\n B, S, C = x_enc.shape\n \n # RevIN\n means = x_enc.mean(1, keepdim=True).detach()\n x_enc = x_enc - means\n stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)\n x_enc /= stdev\n \n # Patching\n x_enc = x_enc.transpose(1, 2) # [B, C, S]\n # padding\n x_enc = F.pad(x_enc, (0, self.padding), mode='replicate')\n x_enc = x_enc.unfold(dimension=-1, size=self.patch_len, step=self.stride)\n # [B, C, P, patch_len]\n \n x_enc = self.patch_emb(x_enc) # [B, C, P, d_model]\n x_enc = x_enc + self.pos_emb # Broadcasts over B, C\n \n # Time processing\n x_time = x_enc.reshape(B * C, self.patch_num, self.d_model)\n x_time = self.time_encoder(x_time) # [B*C, P, d_model]\n \n # Variable processing\n x_var = x_time.reshape(B, C, self.patch_num * self.d_model)\n \n # Attention over variables\n x_var_norm = self.norm_var(x_var)\n attn_out, _ = self.var_attn(x_var_norm, x_var_norm, x_var_norm)\n x_var = x_var + attn_out\n \n x_var = x_var + self.var_ffn(self.norm_var_out(x_var))\n \n # Prediction\n dec_out = self.head(x_var) # [B, C, pred_len]\n dec_out = dec_out.transpose(1, 2) # [B, pred_len, C]\n \n # RevIN\n dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))\n dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))\n \n return dec_out\n\n def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):\n if self.task_name in ['long_term_forecast', 'short_term_forecast']:\n dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)\n return dec_out[:, -self.pred_len:, :]\n return None",
6 "op": "replace",
7 "filename": "Time-Series-Library/models/Custom.py"
8}Tool ResultStep 5
OK: Replaced lines 2..114 with 115 line(s) in Time-Series-Library/models/Custom.py. Editable range: entire file.
[Current file: Time-Series-Library/models/Custom.py | editable: 1–116 | total: 116 lines]
1 import torch
2 import torch.nn as nn
3 import torch.nn.functional as F
4 import math
5
6 class Model(nn.Module):
7 """
8 Combined Patch-Time and Variable-Attention Model for Exogenous Forecasting
9 1. Patching sequences and applying Transformer over Time (Patches)
10 2. Flattening temporal representations and applying Self-Attention over Variables
11 """
12 def __init__(self, configs):
13 super(Model, self).__init__()
14 self.task_name = configs.task_name
15 self.seq_len = configs.seq_len
16 self.pred_len = configs.pred_len
17 self.enc_in = configs.enc_in
18 self.c_out = configs.c_out
19
20
AssistantStep 6test
Tool ResultStep 6
[Test #3] (0 tests remaining) ### ETTh1 (scripts/etth1.sh) Training metrics (last epochs): TRAIN_METRICS epoch=1 train_loss=0.1670774 vali_loss=0.1013860 test_loss=0.0577693 TRAIN_METRICS epoch=2 train_loss=0.1514093 vali_loss=0.1062693 test_loss=0.0597888 TRAIN_METRICS epoch=3 train_loss=0.1349209 vali_loss=0.1149634 test_loss=0.0644557 TRAIN_METRICS epoch=4 train_loss=0.1176272 vali_loss=0.1190505 test_loss=0.0657589 Test results (ETTh1): MSE: 0.057392, MAE: 0.182324 ### Weather (scripts/weather.sh) Training metrics (last epochs): TRAIN_METRICS epoch=3 train_loss=0.9421320 vali_loss=0.0039010 test_loss=0.0014766 TRAIN_METRICS epoch=4 train_loss=0.9065192 vali_loss=0.0034826 test_loss=0.0013545 TRAIN_METRICS epoch=5 train_loss=0.8944274 vali_loss=0.0036887 test_loss=0.0014095 TRAIN_METRICS epoch=6 train_loss=0.8891430 vali_loss=0.0035408 test_loss=0.0013703 TRAIN_METRICS epoch=7 train_loss=0.8870484 vali_loss=0.0035762 test_loss=0.0013808 Test results (Weather): MSE: 0.001357, M