add TiDE and its ETTh1 script

exp/exp_basic.py

@@ -2,7 +2,7 @@
 import torch
 from models import Autoformer, Transformer, TimesNet, Nonstationary_Transformer, DLinear, FEDformer, \
     Informer, LightTS, Reformer, ETSformer, Pyraformer, PatchTST, MICN, Crossformer, FiLM, iTransformer, \
-    Koopa
+    Koopa, TiDE
 
 
 class Exp_Basic(object):
@@ -26,6 +26,7 @@ def __init__(self, args):
             'FiLM': FiLM,
             'iTransformer': iTransformer,
             'Koopa': Koopa,
+            'TiDE': TiDE,
         }
         self.device = self._acquire_device()
         self.model = self._build_model().to(self.device)

models/TiDE.py

@@ -0,0 +1,98 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class LayerNorm(nn.Module):
+    """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
+
+    def __init__(self, ndim, bias):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(ndim))
+        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
+
+    def forward(self, input):
+        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
+
+
+
+class ResBlock(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim, dropout=0.1, bias=True): 
+        super().__init__()
+
+        self.fc1 = nn.Linear(input_dim, hidden_dim, bias=bias) 
+        self.fc2 = nn.Linear(hidden_dim, output_dim, bias=bias)
+        self.fc3 = nn.Linear(input_dim, output_dim, bias=bias)
+        self.dropout = nn.Dropout(dropout)
+        self.relu = nn.ReLU()
+        self.ln = LayerNorm(output_dim, bias=bias)
+
+    def forward(self, x):
+
+        out = self.fc1(x)
+        out = self.relu(out)
+        out = self.fc2(out)
+        out = self.dropout(out)
+        out = out + self.fc3(x)
+        out = self.ln(out)
+        return out
+
+
+#TiDE
+class Model(nn.Module):  
+    '''paper: https://arxiv.org/pdf/2304.08424.pdf '''
+    def __init__(self, configs, bias=True,feature_encode_dim=2): 
+        super(Model, self).__init__()
+        self.configs = configs
+        self.task_name = configs.task_name
+        self.seq_len = configs.seq_len  #L 
+        self.label_len = configs.label_len
+        self.pred_len = configs.pred_len  #H 
+        self.hidden_dim=configs.d_model
+        self.res_hidden=configs.d_model 
+        self.encoder_num=configs.e_layers
+        self.decoder_num=configs.d_layers
+        self.freq=configs.freq
+        self.feature_encode_dim=feature_encode_dim
+        self.decode_dim = configs.c_out
+        self.temporalDecoderHidden=configs.d_ff
+        dropout=configs.dropout
+
+
+        freq_map = {'h': 4, 't': 5, 's': 6,
+                    'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3}
+
+        self.feature_dim=freq_map[self.freq]
+
+
+        flatten_dim = self.seq_len + (self.seq_len + self.pred_len) * self.feature_encode_dim
+
+        self.feature_encoder = ResBlock(self.feature_dim, self.res_hidden, self.feature_encode_dim, dropout, bias)
+        self.encoders = nn.Sequential(ResBlock(flatten_dim, self.res_hidden, self.hidden_dim, dropout, bias),*([ ResBlock(self.hidden_dim, self.res_hidden, self.hidden_dim, dropout, bias)]*(self.encoder_num-1)))
+        self.decoders = nn.Sequential(*([ ResBlock(self.hidden_dim, self.res_hidden, self.hidden_dim, dropout, bias)]*(self.decoder_num-1)),ResBlock(self.hidden_dim, self.res_hidden, self.decode_dim * self.pred_len, dropout, bias))
+        self.temporalDecoder = ResBlock(self.decode_dim + self.feature_encode_dim, self.temporalDecoderHidden, 1, dropout, bias)
+        self.residual_proj = nn.Linear(self.seq_len, self.pred_len, bias=bias)
+
+
+    def forecast(self, x_enc, x_mark_enc, x_dec, batch_y_mark):
+
+        feature = self.feature_encoder(batch_y_mark)
+        hidden = self.encoders(torch.cat([x_enc, feature.reshape(feature.shape[0], -1)], dim=-1))
+        decoded = self.decoders(hidden).reshape(hidden.shape[0], self.pred_len, self.decode_dim)
+        prediction = self.temporalDecoder(torch.cat([feature[:,self.seq_len:], decoded], dim=-1)).squeeze(-1) + self.residual_proj(x_enc)
+        return prediction
+
+    def forward(self, x_enc, x_mark_enc, x_dec, batch_y_mark):
+
+        '''x_mark_enc is the exogenous dynamic feature described in the original paper'''
+        if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
+                batch_y_mark=torch.concat([x_mark_enc,batch_y_mark[:,-self.pred_len:,:]],dim=1)
+                dec_out = torch.stack([self.forecast(x_enc[:,:,feature], x_mark_enc, x_dec, batch_y_mark) for feature in range(x_enc.shape[-1])],dim=-1)
+                return dec_out # [B, L, D]
+
+        return None
+
+
+
+
+

scripts/long_term_forecast/ETT_script/TiDE_ETTh1.sh

@@ -0,0 +1,112 @@
+export CUDA_VISIBLE_DEVICES=2
+
+model_name=TiDE
+
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTh1.csv \
+  --model_id ETTh1_96_96 \
+  --model $model_name \
+  --data ETTh1 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len 96 \
+  --e_layers 2 \
+  --d_layers 2 \
+  --enc_in 7 \
+  --dec_in 7 \
+  --c_out 8 \
+  --d_model 256 \
+  --d_ff 256 \
+  --dropout 0.3 \
+  --batch_size 512 \
+  --learning_rate 0.1 \
+  --patience 5 \
+  --train_epochs 10 \
+
+
+
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTh1.csv \
+  --model_id ETTh1_96_192 \
+  --model $model_name \
+  --data ETTh1 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len 192 \
+  --e_layers 2 \
+  --d_layers 2 \
+  --enc_in 7 \
+  --dec_in 7 \
+  --c_out 8 \
+  --d_model 256 \
+  --d_ff 256 \
+  --dropout 0.3 \
+  --batch_size 512 \
+  --learning_rate 0.1 \
+  --patience 5 \
+  --train_epochs 10 \
+
+
+
+
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTh1.csv \
+  --model_id ETTh1_96_336 \
+  --model $model_name \
+  --data ETTh1 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len 336 \
+  --e_layers 2 \
+  --d_layers 2 \
+  --enc_in 7 \
+  --dec_in 7 \
+  --c_out 8 \
+  --d_model 256 \
+  --d_ff 256 \
+  --dropout 0.3 \
+  --batch_size 512 \
+  --learning_rate 0.1 \
+  --patience 5 \
+  --train_epochs 10 \
+
+
+
+
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTh1.csv \
+  --model_id ETTh1_96_720 \
+  --model $model_name \
+  --data ETTh1 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len 720 \
+  --e_layers 2 \
+  --d_layers 2 \
+  --enc_in 7 \
+  --dec_in 7 \
+  --c_out 8 \
+  --d_model 256 \
+  --d_ff 256 \
+  --dropout 0.3 \
+  --batch_size 512 \
+  --learning_rate 0.1 \
+  --patience 5 \
+  --train_epochs 10 \
+

utils/tools.py

@@ -4,6 +4,7 @@
 import torch
 import matplotlib.pyplot as plt
 import pandas as pd
+import math
 
 plt.switch_backend('agg')
 
@@ -17,6 +18,8 @@ def adjust_learning_rate(optimizer, epoch, args):
             2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6,
             10: 5e-7, 15: 1e-7, 20: 5e-8
         }
+    elif args.lradj == "cosine":
+        lr_adjust = {epoch: args.learning_rate /2 * (1 + math.cos(epoch / args.train_epochs * math.pi))}
     if epoch in lr_adjust.keys():
         lr = lr_adjust[epoch]
         for param_group in optimizer.param_groups: