Agent Conversation: openai/gpt-5.4-pro
Model: openai/gpt-5.4-pro
Total steps: 5
Messages: 11
UserStep 0
Task: ts-long-term-forecast
Long-Term Time Series Forecasting: Custom Model Design
Objective
Design and implement a custom deep learning model for multivariate long-term time series forecasting. 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 multivariate datasets:
- ETTh1 (7 variables, hourly electricity transformer temperature)
- Weather (21 variables, weather observations)
- ECL (321 variables, electricity consumption)
All use seq_len=96, pred_len=96. Metrics: MSE and MAE (lower is better).
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 long-term time series forecasting.
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] — input time series
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: - mask: optional binary mask
15:
16: Must return: [batch, pred_len, c_out] for forecasting
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: given input sequence, predict future values.
31: Input: x_enc [batch, seq_len, enc_in]
32: Output: [batch, pred_len, c_out]
33: """
34: # TODO: Implement your forecasting logic
35: # Placeholder: simple linear projection
36: batch_size = x_enc.shape[0]
37: return torch.zeros(batch_size, self.pred_len, self.c_out).to(x_enc.device)
38:
39: def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
40: if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
41: dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
42: return dec_out[:, -self.pred_len:, :]
43: return NoneTime-Series-Library/models/DLinear.py [READ-ONLY — do not edit]
1: import torch
2: import torch.nn as nn
3: import torch.nn.functional as F
4: from layers.Autoformer_EncDec import serieAssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "filename": "Time-Series-Library/models/Custom.py",
4 "content": "import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom layers.Embed import DataEmbedding_inverted\nfrom layers.SelfAttention_Family import FullAttention, AttentionLayer\nfrom layers.Transformer_EncDec import Encoder, EncoderLayer\n\n\nclass Model(nn.Module):\n \"\"\"\n Adaptive Spectral-Routed Multi-Scale Decomposition forecaster.\n\n The model combines two complementary ideas:\n 1) A bank of decomposition-linear experts at different smoothing scales.\n Each variable is routed to a soft mixture of experts using simple,\n differentiable statistics computed from the current sample.\n 2) A gated inverted-transformer correction branch that operates on the\n routed seasonal signal to capture cross-variate dependencies.\n\n This keeps the strong low-bias behavior of linear forecasting while adding\n a sample-adaptive multivariate correction only where it is useful.\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 experts ---------\n self.kernels = [3, 7, 15, 31]\n self.num_experts = len(self.kernels)\n\n self.linear_seasonal = nn.ModuleList(\n [nn.Linear(self.seq_len, self.pred_len) for _ in range(self.num_experts)]\n )\n self.linear_trend = nn.ModuleList(\n [nn.Linear(self.seq_len, self.pred_len) for _ in range(self.num_experts)]\n )\n self._init_linear_experts()\n\n # Routing is shared across variables; it depends on per-variable sample statistics.\n router_hidden = 16\n stat_dim = 4 # volatility, seasonal strength, spectral entropy, raw scale\n self.expert_router = nn.Sequential(\n nn.Linear(stat_dim, router_hidden),\n nn.GELU(),\n nn.Linear(router_hidden, self.num_experts)\n )\n self.correction_router = nn.Sequential(\n nn.Linear(stat_dim, router_hidden),\n nn.GELU(),\n nn.Linear(router_hidden, 1)\n )\n nn.init.constant_(self.correction_router[-1].bias, -1.5)\n\n # --------- Cross-variate correction branch (inverted transformer) ---------\n d_model = getattr(configs, 'd_model', 128)\n dropout = getattr(configs, 'dropout', 0.1)\n factor = getattr(configs, 'factor', 5)\n n_heads = getattr(configs, 'n_heads', 8)\n d_ff = getattr(configs, 'd_ff', 4 * d_model)\n e_layers = getattr(configs, 'e_layers', 2)\n activation = getattr(configs, 'activation', 'gelu')\n embed = getattr(configs, 'embed', 'timeF')\n freq = getattr(configs, 'freq', 'h')\n\n self.enc_embedding = DataEmbedding_inverted(self.seq_len, d_model, embed, freq, dropout)\n self.encoder = Encoder(\n [\n EncoderLayer(\n AttentionLayer(\n FullAttention(False, factor, attention_dropout=dropout, output_attention=False),\n d_model,\n n_heads,\n ),\n d_model,\n d_ff,\n dropout=dropout,\n activation=activation,\n )\n for _ in range(e_layers)\n ],\n norm_layer=nn.LayerNorm(d_model),\n )\n self.correction_projection = nn.Linear(d_model, self.pred_len, bias=True)\n nn.init.xavier_uniform_(self.correction_projection.weight, gain=0.2)\n if self.correction_projection.bias is not None:\n nn.init.zeros_(self.correction_projection.bias)\n\n def _init_linear_experts(self):\n init_w = (1.0 / self.seq_len) * torch.ones(self.pred_len, self.seq_len)\n for linear in list(self.linear_seasonal) + list(self.linear_trend):\n linear.weight = nn.Parameter(init_w.clone())\n if linear.bias is not None:\n nn.init.zeros_(linear.bias)\n\n @staticmethod\n def _moving_average(x, kernel_size):\n if kernel_size <= 1:\n return x\n pad = (kernel_size - 1) // 2\n x_t = x.transpose(1, 2)\n x_t = F.pad(x_t, (pad, pad), mode='replicate')\n trend = F.avg_pool1d(x_t, kernel_size=kernel_size, stride=1)\n return trend.transpose(1, 2)\n\n @staticmethod\n def _spectral_entropy(x):\n # x: [B, L, N]\n x_fft = torch.fft.rfft(x.float(), dim=1)\n power = x_fft.abs().pow(2) + 1e-6\n power = power / power.sum(dim=1, keepdim=True)\n entropy = -(power * power.log()).sum(dim=1)\n entropy = entropy / math.log(power.shape[1] + 1.0)\n return entropy.to(x.dtype)\n\n def _compute_routing_stats(self, x_norm, raw_stdev):\n # x_norm: [B, L, N], raw_stdev: [B, 1, N]\n diff_energy = (x_norm[:, 1:, :] - x_norm[:, :-1, :]).abs().mean(dim=1)\n local_trend = self._moving_average(x_norm, 7)\n seasonal_strength = (x_norm - local_trend).abs().mean(dim=1)\n entropy = self._spectral_entropy(x_norm)\n scale = torch.log1p(raw_stdev[:, 0, :])\n return torch.stack([diff_energy, seasonal_strength, entropy, scale], dim=-1)\n\n def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):\n \"\"\"\n Input: x_enc [B, seq_len, enc_in]\n Output: [B, pred_len, c_out]\n \"\"\"\n means = x_enc.mean(dim=1, keepdim=True).detach()\n x_centered = x_enc - means\n stdev = torch.sqrt(torch.var(x_centered, dim=1, keepdim=True, unbiased=False) + 1e-5).detach()\n x_norm = x_centered / stdev\n\n # --------- Sample-adaptive routing over decomposition experts ---------\n routing_stats = self._compute_routing_stats(x_norm, stdev)\n expert_logits = self.expert_router(routing_stats) # [B, N, K]\n expert_weights = torch.softmax(expert_logits, dim=-1)\n\n base_forecast = 0.0\n routed_seasonal = 0.0\n for i, kernel in enumerate(self.kernels):\n trend = self._moving_average(x_norm, kernel)\n seasonal = x_norm - trend\n\n seasonal_out = self.linear_seasonal[i](seasonal.permute(0, 2, 1))\n trend_out = self.linear_trend[i](trend.permute(0, 2, 1))\n expert_pred = (seasonal_out + trend_out).permute(0, 2, 1) # [B, pred_len, N]\n\n weight = expert_weights[:, :, i].unsqueeze(1) # [B, 1, N]\n base_forecast = base_forecast + expert_pred * weight\n routed_seasonal = routed_seasonal + seasonal * weight\n\n # --------- Gated multivariate correction on routed seasonal signal ---------\n corr_gate = torch.sigmoid(self.correction_router(routing_stats)).transpose(1, 2) # [B, 1, N]\n\n enc_out = self.enc_embedding(routed_seasonal, x_mark_enc)\n enc_out, _ = self.encoder(enc_out, attn_mask=None)\n correction = self.correction_projection(enc_out).permute(0, 2, 1)\n correction = correction[:, :, :self.enc_in]\n\n dec_out = base_forecast + corr_gate * correction\n\n # De-normalization\n dec_out = dec_out * stdev[:, 0, :].unsqueeze(1)\n dec_out = dec_out + means[:, 0, :].unsqueeze(1)\n return dec_out[:, :, :self.c_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",
5 "after_line": 0,
6 "start_line": 1,
7 "end_line": 43
8}Tool ResultStep 1
OK: Replaced lines 1..43 with 179 line(s) in Time-Series-Library/models/Custom.py. Editable range: entire file.
[Current file: Time-Series-Library/models/Custom.py | editable: 1–179 | total: 179 lines]
1 import math
2 import torch
3 import torch.nn as nn
4 import torch.nn.functional as F
5
6 from layers.Embed import DataEmbedding_inverted
7 from layers.SelfAttention_Family import FullAttention, AttentionLayer
8 from layers.Transformer_EncDec import Encoder, EncoderLayer
9
10
11 class Model(nn.Module):
12 """
13 Adaptive Spectral-Routed Multi-Scale Decomposition forecaster.
14
15 The model combines two complementary ideas:
16 1) A bank of decomposition-linear experts at different smoothing scales.
17 Each variable is routed to a soft mixture of experts using simple,
18 differentiable statistics computed from the current sample.
19 2) A gated inve
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=3 train_loss=0.3418571 vali_loss=0.6991154 test_loss=0.3843020 TRAIN_METRICS epoch=4 train_loss=0.3361335 vali_loss=0.6816290 test_loss=0.3861615 TRAIN_METRICS epoch=5 train_loss=0.3310328 vali_loss=0.6851273 test_loss=0.3866382 TRAIN_METRICS epoch=6 train_loss=0.3275865 vali_loss=0.6844716 test_loss=0.3874595 TRAIN_METRICS epoch=7 train_loss=0.3281729 vali_loss=0.6934118 test_loss=0.3879375 Test results (ETTh1): MSE: 0.385610, MAE: 0.401228 ### Weather (scripts/weather.sh) Training metrics (last epochs): TRAIN_METRICS epoch=6 train_loss=0.4223092 vali_loss=0.4240804 test_loss=0.1765616 TRAIN_METRICS epoch=7 train_loss=0.4211349 vali_loss=0.4247057 test_loss=0.1764166 TRAIN_METRICS epoch=8 train_loss=0.4203675 vali_loss=0.4226846 test_loss=0.1759081 TRAIN_METRICS epoch=9 train_loss=0.4201404 vali_loss=0.4232140 test_loss=0.1760877 TR
AssistantStep 3edit
Tool Input
1{
2 "op": "replace",
3 "filename": "Time-Series-Library/models/Custom.py",
4 "content": "import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom layers.Embed import DataEmbedding_inverted\nfrom layers.SelfAttention_Family import FullAttention, AttentionLayer\nfrom layers.Transformer_EncDec import Encoder, EncoderLayer\n\n\nclass Model(nn.Module):\n \"\"\"\n Adaptive Spectral-Routed Multi-Scale Decomposition forecaster.\n\n Core contribution:\n - a bank of decomposition-linear experts with different smoothing scales,\n - variable-wise routing driven by spectral/temporal statistics,\n - an explicit smoothness prior over expert selection,\n - a gated inverted-transformer correction branch for cross-variate residuals.\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 experts ---------\n self.kernels = [3, 7, 15, 31]\n self.num_experts = len(self.kernels)\n self.register_buffer(\n 'kernel_positions',\n torch.linspace(0.0, 1.0, self.num_experts).view(1, 1, self.num_experts),\n )\n\n self.linear_seasonal = nn.ModuleList(\n [nn.Linear(self.seq_len, self.pred_len) for _ in range(self.num_experts)]\n )\n self.linear_trend = nn.ModuleList(\n [nn.Linear(self.seq_len, self.pred_len) for _ in range(self.num_experts)]\n )\n self._init_linear_experts()\n\n # Statistics: volatility, seasonal strength, trend strength,\n # spectral entropy, low-frequency ratio.\n router_hidden = 16\n stat_dim = 5\n self.expert_router = nn.Sequential(\n nn.Linear(stat_dim, router_hidden),\n nn.GELU(),\n nn.Linear(router_hidden, self.num_experts)\n )\n self.correction_router = nn.Sequential(\n nn.Linear(stat_dim, router_hidden),\n nn.GELU(),\n nn.Linear(router_hidden, 1)\n )\n nn.init.constant_(self.correction_router[-1].bias, -1.2)\n\n # --------- Cross-variate correction branch (inverted transformer) ---------\n d_model = getattr(configs, 'd_model', 128)\n dropout = getattr(configs, 'dropout', 0.1)\n factor = getattr(configs, 'factor', 5)\n n_heads = getattr(configs, 'n_heads', 8)\n d_ff = getattr(configs, 'd_ff', 4 * d_model)\n e_layers = getattr(configs, 'e_layers', 2)\n activation = getattr(configs, 'activation', 'gelu')\n embed = getattr(configs, 'embed', 'timeF')\n freq = getattr(configs, 'freq', 'h')\n\n self.enc_embedding = DataEmbedding_inverted(self.seq_len, d_model, embed, freq, dropout)\n self.encoder = Encoder(\n [\n EncoderLayer(\n AttentionLayer(\n FullAttention(False, factor, attention_dropout=dropout, output_attention=False),\n d_model,\n n_heads,\n ),\n d_model,\n d_ff,\n dropout=dropout,\n activation=activation,\n )\n for _ in range(e_layers)\n ],\n norm_layer=nn.LayerNorm(d_model),\n )\n self.correction_projection = nn.Linear(d_model, self.pred_len, bias=True)\n nn.init.xavier_uniform_(self.correction_projection.weight, gain=0.2)\n if self.correction_projection.bias is not None:\n nn.init.zeros_(self.correction_projection.bias)\n\n def _init_linear_experts(self):\n init_w = (1.0 / self.seq_len) * torch.ones(self.pred_len, self.seq_len)\n for linear in list(self.linear_seasonal) + list(self.linear_trend):\n linear.weight = nn.Parameter(init_w.clone())\n if linear.bias is not None:\n nn.init.zeros_(linear.bias)\n\n @staticmethod\n def _moving_average(x, kernel_size):\n if kernel_size <= 1:\n return x\n pad = (kernel_size - 1) // 2\n x_t = x.transpose(1, 2)\n x_t = F.pad(x_t, (pad, pad), mode='replicate')\n trend = F.avg_pool1d(x_t, kernel_size=kernel_size, stride=1)\n return trend.transpose(1, 2)\n\n @staticmethod\n def _spectral_statistics(x):\n # x: [B, L, N]\n x_fft = torch.fft.rfft(x.float(), dim=1)\n power = x_fft.abs().pow(2) + 1e-6\n total_power = power.sum(dim=1) # [B, N]\n\n power_prob = power / total_power.unsqueeze(1)\n entropy = -(power_prob * power_prob.log()).sum(dim=1)\n entropy = entropy / math.log(power.shape[1] + 1.0)\n\n low_bins = min(4, power.shape[1] - 1)\n if low_bins > 0:\n low_freq_ratio = power[:, 1:1 + low_bins, :].sum(dim=1) / total_power\n else:\n low_freq_ratio = torch.zeros_like(total_power)\n\n return entropy.to(x.dtype), low_freq_ratio.to(x.dtype)\n\n def _compute_routing_stats(self, x_norm):\n # x_norm: [B, L, N]\n diff_energy = (x_norm[:, 1:, :] - x_norm[:, :-1, :]).abs().mean(dim=1)\n local_trend = self._moving_average(x_norm, 7)\n global_trend = self._moving_average(x_norm, 31)\n seasonal_strength = (x_norm - local_trend).abs().mean(dim=1)\n trend_strength = global_trend.abs().mean(dim=1)\n entropy, low_freq_ratio = self._spectral_statistics(x_norm)\n\n stats = torch.stack(\n [diff_energy, seasonal_strength, trend_strength, entropy, low_freq_ratio],\n dim=-1,\n )\n target_scale = 0.5 * low_freq_ratio + 0.5 * (\n trend_strength / (trend_strength + seasonal_strength + 1e-5)\n )\n return stats, target_scale.clamp(0.0, 1.0)\n\n def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):\n \"\"\"\n Input: x_enc [B, seq_len, enc_in]\n Output: [B, pred_len, c_out]\n \"\"\"\n means = x_enc.mean(dim=1, keepdim=True).detach()\n x_centered = x_enc - means\n stdev = torch.sqrt(torch.var(x_centered, dim=1, keepdim=True, unbiased=False) + 1e-5).detach()\n x_norm = x_centered / stdev\n\n # --------- Spectral-routed decomposition experts ---------\n routing_stats, target_scale = self._compute_routing_stats(x_norm)\n smoothness_prior = -4.0 * (self.kernel_positions - target_scale.unsqueeze(-1)).pow(2)\n expert_logits = self.expert_router(routing_stats) + smoothness_prior\n expert_weights = torch.softmax(expert_logits, dim=-1)\n\n base_forecast = 0.0\n routed_seasonal = 0.0\n for i, kernel in enumerate(self.kernels):\n trend = self._moving_average(x_norm, kernel)\n seasonal = x_norm - trend\n\n seasonal_out = self.linear_seasonal[i](seasonal.permute(0, 2, 1))\n trend_out = self.linear_trend[i](trend.permute(0, 2, 1))\n expert_pred = (seasonal_out + trend_out).permute(0, 2, 1) # [B, pred_len, N]\n\n weight = expert_weights[:, :, i].unsqueeze(1) # [B, 1, N]\n base_forecast = base_forecast + expert_pred * weight\n routed_seasonal = routed_seasonal + seasonal * weight\n\n # --------- Gated multivariate correction ---------\n corr_gate = torch.sigmoid(self.correction_router(routing_stats)).transpose(1, 2) # [B, 1, N]\n corr_input = routed_seasonal + 0.25 * (x_norm - routed_seasonal)\n\n enc_out = self.enc_embedding(corr_input, x_mark_enc)\n enc_out, _ = self.encoder(enc_out, attn_mask=None)\n correction = self.correction_projection(enc_out).permute(0, 2, 1)\n correction = correction[:, :, :self.enc_in]\n\n dec_out = base_forecast + corr_gate * correction\n\n # De-normalization\n dec_out = dec_out * stdev[:, 0, :].unsqueeze(1)\n dec_out = dec_out + means[:, 0, :].unsqueeze(1)\n return dec_out[:, :, :self.c_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",
5 "after_line": 0,
6 "start_line": 1,
7 "end_line": 179
8}Tool ResultStep 3
OK: Replaced lines 1..179 with 200 line(s) in Time-Series-Library/models/Custom.py. Editable range: entire file.
[Current file: Time-Series-Library/models/Custom.py | editable: 1–200 | total: 200 lines]
1 import math
2 import torch
3 import torch.nn as nn
4 import torch.nn.functional as F
5
6 from layers.Embed import DataEmbedding_inverted
7 from layers.SelfAttention_Family import FullAttention, AttentionLayer
8 from layers.Transformer_EncDec import Encoder, EncoderLayer
9
10
11 class Model(nn.Module):
12 """
13 Adaptive Spectral-Routed Multi-Scale Decomposition forecaster.
14
15 Core contribution:
16 - a bank of decomposition-linear experts with different smoothing scales,
17 - variable-wise routing driven by spectral/temporal statistics,
18 - an explicit smoothness prior over expert selection,
19 - a gated inverted-transformer correction branch for
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=0.3907816 vali_loss=0.7016570 test_loss=0.3879403 TRAIN_METRICS epoch=2 train_loss=0.3457690 vali_loss=0.6845376 test_loss=0.3904530 TRAIN_METRICS epoch=3 train_loss=0.3295128 vali_loss=0.7062310 test_loss=0.3899037 TRAIN_METRICS epoch=4 train_loss=0.3210375 vali_loss=0.6983339 test_loss=0.3925247 TRAIN_METRICS epoch=5 train_loss=0.3171876 vali_loss=0.6996834 test_loss=0.3914314 Test results (ETTh1): MSE: 0.390036, MAE: 0.404435 ### Weather (scripts/weather.sh) Training metrics (last epochs): TRAIN_METRICS epoch=5 train_loss=0.4146207 vali_loss=0.4201717 test_loss=0.1726959 TRAIN_METRICS epoch=6 train_loss=0.4122156 vali_loss=0.4188442 test_loss=0.1722429 TRAIN_METRICS epoch=7 train_loss=0.4110851 vali_loss=0.4196927 test_loss=0.1722431 TRAIN_METRICS epoch=8 train_loss=0.4103108 vali_loss=0.4202230 test_loss=0.1722513 TRA
AssistantStep 5submit
Tool Input
1{
2 "n": 2
3}Tool ResultStep 5
[submit] Submitting result from test #2 as final. ### ETTh1 (scripts/etth1.sh) Training metrics (last epochs): TRAIN_METRICS epoch=1 train_loss=0.3907816 vali_loss=0.7016570 test_loss=0.3879403 TRAIN_METRICS epoch=2 train_loss=0.3457690 vali_loss=0.6845376 test_loss=0.3904530 TRAIN_METRICS epoch=3 train_loss=0.3295128 vali_loss=0.7062310 test_loss=0.3899037 TRAIN_METRICS epoch=4 train_loss=0.3210375 vali_loss=0.6983339 test_loss=0.3925247 TRAIN_METRICS epoch=5 train_loss=0.3171876 vali_loss=0.6996834 test_loss=0.3914314 Test results (ETTh1): MSE: 0.390036, MAE: 0.404435 ### Weather (scripts/weather.sh) Training metrics (last epochs): TRAIN_METRICS epoch=5 train_loss=0.4146207 vali_loss=0.4201717 test_loss=0.1726959 TRAIN_METRICS epoch=6 train_loss=0.4122156 vali_loss=0.4188442 test_loss=0.1722429 TRAIN_METRICS epoch=7 train_loss=0.4110851 vali_loss=0.4196927 test_loss=0.1722431 TRAIN_METRICS epoch=8 train_loss=0.4103108 vali_loss=0.4202230 test_loss=0.1722513 TRAIN_METRICS epoch=9