Agent Conversation: anthropic/claude-opus-4.6
Model: anthropic/claude-opus-4.6
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 "op": "replace",
3 "filename": "Time-Series-Library/models/Custom.py",
4 "start_line": 1,
5 "end_line": 42,
6 "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom layers.Autoformer_EncDec import series_decomp\n\n\nclass Model(nn.Module):\n \"\"\"\n Multi-Scale Decomposition with Cross-Variable Attention (MSDecompXV).\n\n Novel algorithmic contributions:\n 1. Multi-scale series decomposition at different temporal resolutions\n (kernel sizes 13, 25, 49) to capture patterns at different scales,\n with learnable scale combination weights.\n 2. Lightweight cross-variable attention that learns inter-variable\n dependencies to enhance predictions using exogenous information.\n Uses input sequences as queries/keys and predicted values as values.\n 3. Adaptive gating to control the balance between univariate temporal\n patterns and cross-variable information flow, initialized\n conservatively to preserve the strong DLinear-like baseline.\n \"\"\"\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 # --- Hyperparameters ---\n kernel_sizes = [13, 25, 49]\n self.n_scales = len(kernel_sizes)\n d_attn = 32\n self.d_attn = d_attn\n\n # --- Multi-scale decomposition ---\n self.decomp_list = nn.ModuleList([\n series_decomp(k) for k in kernel_sizes\n ])\n\n # --- Per-scale temporal projections (shared across variables) ---\n self.seasonal_linears = nn.ModuleList()\n self.trend_linears = nn.ModuleList()\n for _ in range(self.n_scales):\n s_lin = nn.Linear(self.seq_len, self.pred_len)\n t_lin = nn.Linear(self.seq_len, self.pred_len)\n # Initialize with uniform averaging weights (like DLinear)\n s_lin.weight = nn.Parameter(\n (1.0 / self.seq_len) * torch.ones(self.pred_len, self.seq_len))\n t_lin.weight = nn.Parameter(\n (1.0 / self.seq_len) * torch.ones(self.pred_len, self.seq_len))\n self.seasonal_linears.append(s_lin)\n self.trend_linears.append(t_lin)\n\n # --- Cross-variable attention ---\n # Maps each variable's temporal sequence to a compact representation\n # for computing inter-variable attention weights\n self.cv_query = nn.Linear(self.seq_len, d_attn)\n self.cv_key = nn.Linear(self.seq_len, d_attn)\n # Value projection transforms predictions before cross-variable mixing\n self.cv_value = nn.Linear(self.pred_len, self.pred_len)\n\n # --- Adaptive gating ---\n # Initialized at -1.0 so sigmoid(-1)~0.27, conservatively keeping\n # mostly the base prediction and gradually learning cross-variable usage\n self.gate = nn.Parameter(torch.tensor(-1.0))\n\n # --- Learnable scale combination weights ---\n self.scale_weights = nn.Parameter(torch.ones(self.n_scales))\n\n # --- Dropout for attention regularization ---\n self.dropout = nn.Dropout(0.05)\n\n def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):\n B, L, C = x_enc.shape\n\n # === RevIN Normalization ===\n means = x_enc.mean(1, keepdim=True).detach()\n x_enc = x_enc - means\n stdev = torch.sqrt(\n torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)\n x_enc /= stdev\n\n # === Multi-scale Decomposition + Temporal Projection ===\n scale_preds = []\n for i in range(self.n_scales):\n seasonal, trend = self.decomp_list[i](x_enc) # [B, L, C]\n # Per-variable temporal projection: [B, C, L] -> [B, C, P]\n s_out = self.seasonal_linears[i](seasonal.permute(0, 2, 1))\n t_out = self.trend_linears[i](trend.permute(0, 2, 1))\n scale_preds.append(s_out + t_out) # [B, C, P]\n\n # Adaptive scale combination with softmax weights\n w = F.softmax(self.scale_weights, dim=0)\n base_pred = sum(w[i] * scale_preds[i] for i in range(self.n_scales))\n\n # === Cross-Variable Attention ===\n # Each variable's history acts as a token\n x_t = x_enc.permute(0, 2, 1) # [B, C, L]\n q = self.cv_query(x_t) # [B, C, d_attn]\n k = self.cv_key(x_t) # [B, C, d_attn]\n\n # Scaled dot-product attention across variables\n attn = torch.matmul(q, k.transpose(-1, -2)) / (self.d_attn ** 0.5)\n attn = F.softmax(attn, dim=-1) # [B, C, C]\n attn = self.dropout(attn)\n\n # Cross-variable mixing of predicted values\n v = self.cv_value(base_pred) # [B, C, P]\n cv_out = torch.matmul(attn, v) # [B, C, P]\n\n # === Gated Combination ===\n g = torch.sigmoid(self.gate)\n pred = (1 - g) * base_pred + g * cv_out # [B, C, P]\n\n # [B, P, C]\n dec_out = pred.permute(0, 2, 1)\n\n # === RevIN De-normalization ===\n dec_out = dec_out * stdev[:, 0, :].unsqueeze(1).repeat(\n 1, self.pred_len, 1)\n dec_out = dec_out + means[:, 0, :].unsqueeze(1).repeat(\n 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 == 'long_term_forecast' or self.task_name == '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\n"
7}Tool ResultStep 1
OK: Replaced lines 1..42 with 132 line(s) in Time-Series-Library/models/Custom.py. Editable range: entire file.
[Current file: Time-Series-Library/models/Custom.py | editable: 1–132 | total: 132 lines]
1 import torch
2 import torch.nn as nn
3 import torch.nn.functional as F
4 from layers.Autoformer_EncDec import series_decomp
5
6
7 class Model(nn.Module):
8 """
9 Multi-Scale Decomposition with Cross-Variable Attention (MSDecompXV).
10
11 Novel algorithmic contributions:
12 1. Multi-scale series decomposition at different temporal resolutions
13 (kernel sizes 13, 25, 49) to capture patterns at different scales,
14 with learnable scale combination weights.
15 2. Lightweight cross-variable attention that learns inter-variable
16 dependencies to enhance predictions using exogenous information.
17 Uses input sequences as queries/keys and predicted
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=4 train_loss=0.1660783 vali_loss=0.0995498 test_loss=0.0563721 TRAIN_METRICS epoch=5 train_loss=0.1655747 vali_loss=0.0992715 test_loss=0.0563107 TRAIN_METRICS epoch=6 train_loss=0.1650217 vali_loss=0.0997974 test_loss=0.0562933 TRAIN_METRICS epoch=7 train_loss=0.1652756 vali_loss=0.0996934 test_loss=0.0562788 TRAIN_METRICS epoch=8 train_loss=0.1644933 vali_loss=0.0992723 test_loss=0.0562687 Test results (ETTh1): MSE: 0.055932, MAE: 0.178971 ### Weather (scripts/weather.sh) Training metrics (last epochs): TRAIN_METRICS epoch=1 train_loss=1.1668388 vali_loss=0.0031887 test_loss=0.0011906 TRAIN_METRICS epoch=2 train_loss=1.0019061 vali_loss=0.0033155 test_loss=0.0012661 TRAIN_METRICS epoch=3 train_loss=0.9655816 vali_loss=0.0033268 test_loss=0.0012687 TRAIN_METRICS epoch=4 train_loss=0.9554550 vali_loss=0.0033740 test_loss=0.0012758 Te
AssistantStep 3edit
Tool Input
1{
2 "op": "replace",
3 "filename": "Time-Series-Library/models/Custom.py",
4 "start_line": 1,
5 "end_line": 132,
6 "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom layers.Autoformer_EncDec import series_decomp\n\n\nclass Model(nn.Module):\n \"\"\"\n Multi-Scale Decomposition with Target-Centric Exogenous Attention (MSD-TCA).\n\n Novel algorithmic contributions:\n 1. Multi-scale trend-seasonal decomposition (kernel sizes 15, 25) with\n learnable scale combination to capture temporal patterns at different\n resolutions simultaneously.\n 2. Target-centric cross-variable attention: instead of full C×C attention\n (which is noisy for high-dimensional data like ECL with 321 vars),\n we compute only a [1×C] attention vector where the target variable\n queries all exogenous variables' histories. This focuses the cross-\n variable learning signal entirely on improving the target prediction.\n 3. Additive gating with conservative initialization: the exogenous\n enhancement is interpolated with the base DLinear prediction using\n a learned gate initialized near zero, ensuring the strong DLinear\n baseline is preserved early in training while gradually incorporating\n cross-variable information as beneficial.\n \"\"\"\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 # === Multi-scale decomposition ===\n self.decomp_short = series_decomp(15)\n self.decomp_long = series_decomp(25)\n\n # Per-scale temporal projections (shared across all variables)\n self.s_short = nn.Linear(self.seq_len, self.pred_len)\n self.t_short = nn.Linear(self.seq_len, self.pred_len)\n self.s_long = nn.Linear(self.seq_len, self.pred_len)\n self.t_long = nn.Linear(self.seq_len, self.pred_len)\n\n # Uniform averaging initialization (like DLinear)\n for lin in [self.s_short, self.t_short, self.s_long, self.t_long]:\n lin.weight = nn.Parameter(\n (1.0 / self.seq_len) * torch.ones(self.pred_len, self.seq_len))\n\n # Learnable scale combination weight\n self.scale_alpha = nn.Parameter(torch.tensor(0.0)) # sigmoid→0.5\n\n # === Target-centric cross-variable attention ===\n d_attn = 64\n self.d_attn = d_attn\n # Project each variable's temporal history to attention space\n self.q_proj = nn.Linear(self.seq_len, d_attn)\n self.k_proj = nn.Linear(self.seq_len, d_attn)\n # Transform predictions before cross-variable aggregation\n self.v_proj = nn.Linear(self.pred_len, self.pred_len)\n\n # === Gating: sigmoid(-2.0)≈0.12 → conservative exogenous usage ===\n self.exo_gate = nn.Parameter(torch.tensor(-2.0))\n\n def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):\n B, L, C = x_enc.shape\n\n # === RevIN Normalization ===\n means = x_enc.mean(1, keepdim=True).detach()\n x_enc = x_enc - means\n stdev = torch.sqrt(\n torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)\n x_enc /= stdev\n\n # === Multi-scale Decomposition + Temporal Projection ===\n # Scale 1: Short-term (kernel=15)\n sea_s, tr_s = self.decomp_short(x_enc)\n out_s = (self.s_short(sea_s.permute(0, 2, 1))\n + self.t_short(tr_s.permute(0, 2, 1)))\n\n # Scale 2: Long-term (kernel=25)\n sea_l, tr_l = self.decomp_long(x_enc)\n out_l = (self.s_long(sea_l.permute(0, 2, 1))\n + self.t_long(tr_l.permute(0, 2, 1)))\n\n # Adaptive scale combination\n a = torch.sigmoid(self.scale_alpha)\n base = a * out_s + (1 - a) * out_l # [B, C, P]\n\n # === Target-Centric Cross-Variable Attention ===\n x_t = x_enc.permute(0, 2, 1) # [B, C, L]\n\n # Query: only the target variable's history\n q = self.q_proj(x_t[:, -1:, :]) # [B, 1, d_attn]\n # Keys: all variables' histories\n k = self.k_proj(x_t) # [B, C, d_attn]\n\n # Attention weights: which exogenous variables help the target?\n attn = torch.matmul(q, k.transpose(-1, -2)) # [B, 1, C]\n attn = attn / (self.d_attn ** 0.5)\n attn = F.softmax(attn, dim=-1)\n\n # Values: transformed base predictions of all variables\n v = self.v_proj(base) # [B, C, P]\n exo_pred = torch.matmul(attn, v) # [B, 1, P]\n\n # === Gated Exogenous Enhancement (target only) ===\n g = torch.sigmoid(self.exo_gate)\n # Interpolate: base target pred ←→ exogenous-weighted pred\n target_enhanced = (1 - g) * base[:, -1:, :] + g * exo_pred\n\n # Non-target variables keep their base predictions unchanged\n pred = torch.cat([base[:, :-1, :], target_enhanced], dim=1)\n\n dec_out = pred.permute(0, 2, 1) # [B, P, C]\n\n # === RevIN De-normalization ===\n dec_out = dec_out * stdev[:, 0, :].unsqueeze(1).repeat(\n 1, self.pred_len, 1)\n dec_out = dec_out + means[:, 0, :].unsqueeze(1).repeat(\n 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 == 'long_term_forecast' or self.task_name == '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\n"
7}Tool ResultStep 3
OK: Replaced lines 1..132 with 129 line(s) in Time-Series-Library/models/Custom.py. Editable range: entire file.
[Current file: Time-Series-Library/models/Custom.py | editable: 1–129 | total: 129 lines]
1 import torch
2 import torch.nn as nn
3 import torch.nn.functional as F
4 from layers.Autoformer_EncDec import series_decomp
5
6
7 class Model(nn.Module):
8 """
9 Multi-Scale Decomposition with Target-Centric Exogenous Attention (MSD-TCA).
10
11 Novel algorithmic contributions:
12 1. Multi-scale trend-seasonal decomposition (kernel sizes 15, 25) with
13 learnable scale combination to capture temporal patterns at different
14 resolutions simultaneously.
15 2. Target-centric cross-variable attention: instead of full C×C attention
16 (which is noisy for high-dimensional data like ECL with 321 vars),
17 we compute only a [1×C] attention vector wh
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=4 train_loss=0.1674464 vali_loss=0.1009871 test_loss=0.0566176 TRAIN_METRICS epoch=5 train_loss=0.1672347 vali_loss=0.1000796 test_loss=0.0565880 TRAIN_METRICS epoch=6 train_loss=0.1660986 vali_loss=0.1000849 test_loss=0.0565529 TRAIN_METRICS epoch=7 train_loss=0.1666832 vali_loss=0.1003282 test_loss=0.0565384 TRAIN_METRICS epoch=8 train_loss=0.1657132 vali_loss=0.1007071 test_loss=0.0565411 Test results (ETTh1): MSE: 0.056262, MAE: 0.179550 ### Weather (scripts/weather.sh) Training metrics (last epochs): TRAIN_METRICS epoch=1 train_loss=1.1697956 vali_loss=0.0030737 test_loss=0.0011877 TRAIN_METRICS epoch=2 train_loss=1.0340757 vali_loss=0.0033708 test_loss=0.0012290 TRAIN_METRICS epoch=3 train_loss=0.9766033 vali_loss=0.0032902 test_loss=0.0012494 TRAIN_METRICS epoch=4 train_loss=0.9656049 vali_loss=0.0033314 test_loss=0.0012495 Tes
AssistantStep 5edit
Tool Input
1{
2 "op": "replace",
3 "filename": "Time-Series-Library/models/Custom.py",
4 "start_line": 1,
5 "end_line": 129,
6 "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom layers.Autoformer_EncDec import series_decomp\n\n\nclass Model(nn.Module):\n \"\"\"\n Multi-Scale Decomposition with Target-Centric Exogenous Attention (MSD-TCA).\n\n Novel algorithmic contributions:\n 1. Multi-scale trend-seasonal decomposition (kernels 15, 25) with\n learnable combination weights. The base prediction path preserves\n DLinear's proven design WITHOUT instance normalization, avoiding\n the RevIN-decomposition redundancy that degrades performance on\n high-dimensional datasets (decomposition already handles stationarity).\n 2. Target-centric cross-variable attention: a lightweight [1×C]\n attention mechanism where only the target variable queries all\n exogenous variables' histories (instead of full C×C attention).\n Uses local normalization for scale-invariant key/query computation\n and per-variable prediction normalization for scale-invariant\n cross-variable value aggregation.\n 3. Conservative additive gating (sigmoid(-3)≈0.047) that preserves\n the strong DLinear baseline while allowing gradual incorporation\n of cross-variable exogenous information during training.\n \"\"\"\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 # === Multi-scale DLinear backbone (NO RevIN — matches DLinear) ===\n self.decomp_short = series_decomp(15)\n self.decomp_long = series_decomp(25)\n\n self.s_short = nn.Linear(self.seq_len, self.pred_len)\n self.t_short = nn.Linear(self.seq_len, self.pred_len)\n self.s_long = nn.Linear(self.seq_len, self.pred_len)\n self.t_long = nn.Linear(self.seq_len, self.pred_len)\n\n # Uniform averaging initialization (like DLinear)\n for lin in [self.s_short, self.t_short, self.s_long, self.t_long]:\n lin.weight = nn.Parameter(\n (1.0 / self.seq_len) * torch.ones(self.pred_len, self.seq_len))\n\n # Learnable scale combination\n self.scale_alpha = nn.Parameter(torch.tensor(0.0)) # sigmoid→0.5\n\n # === Target-centric cross-variable attention ===\n d_attn = 32\n self.d_attn = d_attn\n self.q_proj = nn.Linear(self.seq_len, d_attn)\n self.k_proj = nn.Linear(self.seq_len, d_attn)\n self.v_proj = nn.Linear(self.pred_len, self.pred_len)\n\n # Conservative gate: sigmoid(-3)≈0.047\n self.exo_gate = nn.Parameter(torch.tensor(-3.0))\n\n def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):\n B, L, C = x_enc.shape\n\n # === Multi-scale DLinear base (NO RevIN) ===\n # Scale 1: Short-term patterns (kernel=15)\n sea_s, tr_s = self.decomp_short(x_enc)\n out_s = (self.s_short(sea_s.permute(0, 2, 1))\n + self.t_short(tr_s.permute(0, 2, 1)))\n\n # Scale 2: Long-term patterns (kernel=25)\n sea_l, tr_l = self.decomp_long(x_enc)\n out_l = (self.s_long(sea_l.permute(0, 2, 1))\n + self.t_long(tr_l.permute(0, 2, 1)))\n\n # Adaptive scale combination\n a = torch.sigmoid(self.scale_alpha)\n base = a * out_s + (1 - a) * out_l # [B, C, P]\n\n # === Target-Centric Cross-Variable Attention ===\n # Local normalization for scale-invariant attention keys/queries\n means_enc = x_enc.mean(1, keepdim=True).detach()\n stdev_enc = torch.sqrt(\n torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)\n x_norm = (x_enc - means_enc) / stdev_enc\n x_t = x_norm.permute(0, 2, 1) # [B, C, L]\n\n # Query: target | Keys: all variables\n q = self.q_proj(x_t[:, -1:, :]) # [B, 1, d]\n k = self.k_proj(x_t) # [B, C, d]\n attn = F.softmax(\n torch.matmul(q, k.transpose(-1, -2)) / (self.d_attn ** 0.5),\n dim=-1) # [B, 1, C]\n\n # Per-variable prediction normalization for scale-invariant mixing\n base_mean = base.mean(-1, keepdim=True) # [B, C, 1]\n base_std = base.std(-1, keepdim=True) + 1e-5 # [B, C, 1]\n base_normed = (base - base_mean) / base_std\n\n v = self.v_proj(base_normed) # [B, C, P]\n exo_normed = torch.matmul(attn, v) # [B, 1, P]\n\n # De-normalize to target's prediction scale\n target_mean = base_mean[:, -1:, :] # [B, 1, 1]\n target_std = base_std[:, -1:, :] # [B, 1, 1]\n exo = exo_normed * target_std + target_mean\n\n # === Gated enhancement for target variable only ===\n g = torch.sigmoid(self.exo_gate)\n target_pred = (1 - g) * base[:, -1:, :] + g * exo\n\n # Non-target variables keep their base DLinear predictions\n pred = torch.cat([base[:, :-1, :], target_pred], dim=1)\n return pred.permute(0, 2, 1) # [B, P, C]\n\n def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):\n if self.task_name == 'long_term_forecast' or self.task_name == '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\n"
7}Tool ResultStep 5
OK: Replaced lines 1..129 with 121 line(s) in Time-Series-Library/models/Custom.py. Editable range: entire file.
[Current file: Time-Series-Library/models/Custom.py | editable: 1–121 | total: 121 lines]
1 import torch
2 import torch.nn as nn
3 import torch.nn.functional as F
4 from layers.Autoformer_EncDec import series_decomp
5
6
7 class Model(nn.Module):
8 """
9 Multi-Scale Decomposition with Target-Centric Exogenous Attention (MSD-TCA).
10
11 Novel algorithmic contributions:
12 1. Multi-scale trend-seasonal decomposition (kernels 15, 25) with
13 learnable combination weights. The base prediction path preserves
14 DLinear's proven design WITHOUT instance normalization, avoiding
15 the RevIN-decomposition redundancy that degrades performance on
16 high-dimensional datasets (decomposition already handles stationarity).
17 2. Target-centric cr
AssistantStep 6test
Tool ResultStep 6
[Test #3] (0 tests remaining) ### ETTh1 (scripts/etth1.sh) Training metrics (last epochs): TRAIN_METRICS epoch=6 train_loss=0.1677205 vali_loss=0.1014741 test_loss=0.0655239 TRAIN_METRICS epoch=7 train_loss=0.1672991 vali_loss=0.1007223 test_loss=0.0652230 TRAIN_METRICS epoch=8 train_loss=0.1671759 vali_loss=0.0997125 test_loss=0.0652165 TRAIN_METRICS epoch=9 train_loss=0.1673063 vali_loss=0.1007400 test_loss=0.0651507 TRAIN_METRICS epoch=10 train_loss=0.1676558 vali_loss=0.1008330 test_loss=0.0651724 Test results (ETTh1): MSE: 0.064612, MAE: 0.187897 ### Weather (scripts/weather.sh) Training metrics (last epochs): TRAIN_METRICS epoch=6 train_loss=0.9407580 vali_loss=0.0039250 test_loss=0.0057248 TRAIN_METRICS epoch=7 train_loss=0.9402900 vali_loss=0.0039219 test_loss=0.0057107 TRAIN_METRICS epoch=8 train_loss=0.9400491 vali_loss=0.0039194 test_loss=0.0057099 TRAIN_METRICS epoch=9 train_loss=0.9399350 vali_loss=0.0039193 test_loss=0.0057113 TRAIN_METRICS epoch=10 train_loss=0.93990