feat: 🎸 add a LTSF baseline SOFTS (added by @superarthurlx)

GestaltCogTeam · Nov 27, 2024 · c2940f2 · c2940f2
1 parent 47e0070
commit c2940f2
Show file tree

Hide file tree

Showing 6 changed files with 307 additions and 10 deletions.
diff --git a/README.md b/README.md
@@ -134,6 +134,7 @@ The code links (💻Code) in the table below point to the official implementatio
 
 | 📊Baseline    | 📝Title                                                                                                  | 📄Paper                                                | 💻Code                                                                        | 🏛Venue    | 🎯Task |
 | :------------ | :------------------------------------------------------------------------------------------------------- | :----------------------------------------------------- | :---------------------------------------------------------------------------- | :--------- | :----- |
+| SOFTS         | SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion                           | [Link](https://arxiv.org/pdf/2404.14197)                  | [Link](https://github.com/Secilia-Cxy/SOFTS)                                        | NeurIPS'24 | LTSF   |
 | CATS          | Are Self-Attentions Effective for Time Series Forecasting?                                               | [Link](https://arxiv.org/pdf/2405.16877)                  | [Link](https://github.com/dongbeank/CATS)                                        | NeurIPS'24 | LTSF   |
 | Sumba         | Structured Matrix Basis for Multivariate Time Series Forecasting with Interpretable Dynamics             | [Link](https://xiucheng.org/assets/pdfs/nips24-sumba.pdf) | [Link](https://github.com/chenxiaodanhit/Sumba/)                                 | NeurIPS'24 | LTSF   |
 | GLAFF         | Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective | [Link](https://arxiv.org/pdf/2409.18696)                  | [Link](https://github.com/ForestsKing/GLAFF)                                     | NeurIPS'24 | LTSF   |

diff --git a/README_CN.md b/README_CN.md
@@ -135,6 +135,7 @@ BasicTS 实现了丰富的基线模型，包括经典模型、时空预测模型
 
 | 📊Baseline    | 📝Title                                                                                                  | 📄Paper                                                | 💻Code                                                                        | 🏛Venue    | 🎯Task |
 | :------------ | :------------------------------------------------------------------------------------------------------- | :----------------------------------------------------- | :---------------------------------------------------------------------------- | :--------- | :----- |
+| SOFTS         | SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion                           | [Link](https://arxiv.org/pdf/2404.14197)                  | [Link](https://github.com/Secilia-Cxy/SOFTS)                                        | NeurIPS'24 | LTSF   |
 | CATS          | Are Self-Attentions Effective for Time Series Forecasting?                                               | [Link](https://arxiv.org/pdf/2405.16877)                  | [Link](https://github.com/dongbeank/CATS)                                        | NeurIPS'24 | LTSF   |
 | Sumba         | Structured Matrix Basis for Multivariate Time Series Forecasting with Interpretable Dynamics             | [Link](https://xiucheng.org/assets/pdfs/nips24-sumba.pdf) | [Link](https://github.com/chenxiaodanhit/Sumba/)                                 | NeurIPS'24 | LTSF   |
 | GLAFF         | Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective | [Link](https://arxiv.org/pdf/2409.18696)                  | [Link](https://github.com/ForestsKing/GLAFF)                                     | NeurIPS'24 | LTSF   |

diff --git a/baselines/SOFTS/ETTh1.py b/baselines/SOFTS/ETTh1.py
@@ -0,0 +1,149 @@
+import os
+import sys
+from easydict import EasyDict
+sys.path.append(os.path.abspath(__file__ + '/../../..'))
+from basicts.metrics import masked_mae, masked_mse, masked_mape, masked_rmse
+from basicts.data import TimeSeriesForecastingDataset
+from basicts.runners import SimpleTimeSeriesForecastingRunner
+from basicts.scaler import ZScoreScaler
+from basicts.utils import get_regular_settings
+
+from .arch import SOFTS
+
+############################## Hot Parameters ##############################
+# Dataset & Metrics configuration
+DATA_NAME = 'ETTh1'  # Dataset name
+regular_settings = get_regular_settings(DATA_NAME)
+INPUT_LEN = regular_settings['INPUT_LEN']  # 336, better performance
+OUTPUT_LEN = regular_settings['OUTPUT_LEN']  # Length of output sequence
+TRAIN_VAL_TEST_RATIO = regular_settings['TRAIN_VAL_TEST_RATIO']  # Train/Validation/Test split ratios
+NORM_EACH_CHANNEL = regular_settings['NORM_EACH_CHANNEL'] # Whether to normalize each channel of the data
+RESCALE = regular_settings['RESCALE'] # Whether to rescale the data
+NULL_VAL = regular_settings['NULL_VAL'] # Null value in the data
+# Model architecture and parameters
+MODEL_ARCH = SOFTS
+NUM_NODES = 7
+MODEL_PARAM = {
+    "enc_in": NUM_NODES,                        # num nodes
+    "dec_in": NUM_NODES,
+    "c_out": NUM_NODES,
+    "seq_len": INPUT_LEN,
+    "pred_len": OUTPUT_LEN,         # prediction sequence length
+    "e_layers": 2,                              # num of encoder layers
+    "d_model": 256,
+    "d_core": 256,
+    "d_ff": 512,
+    "dropout": 0.0,
+    "use_norm" : True,
+    "activation": "gelu",
+    "num_time_features": 4,                     # number of used time features
+    "time_of_day_size": 24,
+    "day_of_week_size": 7,
+    "day_of_month_size": 31,
+    "day_of_year_size": 366
+    }
+NUM_EPOCHS = 50
+
+############################## General Configuration ##############################
+CFG = EasyDict()
+# General settings
+CFG.DESCRIPTION = 'An Example Config'
+CFG.GPU_NUM = 1 # Number of GPUs to use (0 for CPU mode)
+# Runner
+CFG.RUNNER = SimpleTimeSeriesForecastingRunner
+
+############################## Dataset Configuration ##############################
+CFG.DATASET = EasyDict()
+# Dataset settings
+CFG.DATASET.NAME = DATA_NAME
+CFG.DATASET.TYPE = TimeSeriesForecastingDataset
+CFG.DATASET.PARAM = EasyDict({
+    'dataset_name': DATA_NAME,
+    'train_val_test_ratio': TRAIN_VAL_TEST_RATIO,
+    'input_len': INPUT_LEN,
+    'output_len': OUTPUT_LEN,
+    # 'mode' is automatically set by the runner
+})
+
+############################## Scaler Configuration ##############################
+CFG.SCALER = EasyDict()
+# Scaler settings
+CFG.SCALER.TYPE = ZScoreScaler # Scaler class
+CFG.SCALER.PARAM = EasyDict({
+    'dataset_name': DATA_NAME,
+    'train_ratio': TRAIN_VAL_TEST_RATIO[0],
+    'norm_each_channel': NORM_EACH_CHANNEL,
+    'rescale': RESCALE,
+})
+
+############################## Model Configuration ##############################
+CFG.MODEL = EasyDict()
+# Model settings
+CFG.MODEL.NAME = MODEL_ARCH.__name__
+CFG.MODEL.ARCH = MODEL_ARCH
+CFG.MODEL.PARAM = MODEL_PARAM
+CFG.MODEL.FORWARD_FEATURES = [0, 1, 2, 3, 4]
+CFG.MODEL.TARGET_FEATURES = [0]
+
+############################## Metrics Configuration ##############################
+
+CFG.METRICS = EasyDict()
+# Metrics settings
+CFG.METRICS.FUNCS = EasyDict({
+                                'MAE': masked_mae,
+                                'MSE': masked_mse,
+                                'RMSE': masked_rmse,
+                                'MAPE': masked_mape
+                            })
+CFG.METRICS.TARGET = 'MAE'
+CFG.METRICS.NULL_VAL = NULL_VAL
+
+############################## Training Configuration ##############################
+CFG.TRAIN = EasyDict()
+CFG.TRAIN.NUM_EPOCHS = NUM_EPOCHS
+CFG.TRAIN.CKPT_SAVE_DIR = os.path.join(
+    'checkpoints',
+    MODEL_ARCH.__name__,
+    '_'.join([DATA_NAME, str(CFG.TRAIN.NUM_EPOCHS), str(INPUT_LEN), str(OUTPUT_LEN)])
+)
+CFG.TRAIN.LOSS = masked_mae
+# Optimizer settings
+CFG.TRAIN.OPTIM = EasyDict()
+CFG.TRAIN.OPTIM.TYPE = "Adam"
+CFG.TRAIN.OPTIM.PARAM = {
+    "lr": 0.0003,
+}
+# Learning rate scheduler settings
+CFG.TRAIN.LR_SCHEDULER = EasyDict()
+CFG.TRAIN.LR_SCHEDULER.TYPE = "MultiStepLR"
+CFG.TRAIN.LR_SCHEDULER.PARAM = {
+    "milestones": [1, 25, 50],
+    "gamma": 0.5
+}
+CFG.TRAIN.CLIP_GRAD_PARAM = {
+    'max_norm': 5.0
+}
+# Train data loader settings
+CFG.TRAIN.DATA = EasyDict()
+CFG.TRAIN.DATA.BATCH_SIZE = 64
+CFG.TRAIN.DATA.SHUFFLE = True
+CFG.TRAIN.EARLY_STOPPING_PATIENCE = 10
+
+############################## Validation Configuration ##############################
+CFG.VAL = EasyDict()
+CFG.VAL.INTERVAL = 1
+CFG.VAL.DATA = EasyDict()
+CFG.VAL.DATA.BATCH_SIZE = 64
+
+############################## Test Configuration ##############################
+CFG.TEST = EasyDict()
+CFG.TEST.INTERVAL = 1
+CFG.TEST.DATA = EasyDict()
+CFG.TEST.DATA.BATCH_SIZE = 64
+
+############################## Evaluation Configuration ##############################
+
+CFG.EVAL = EasyDict()
+
+# Evaluation parameters
+CFG.EVAL.USE_GPU = True # Whether to use GPU for evaluation. Default: True
diff --git a/baselines/SOFTS/ETTh2.py b/baselines/SOFTS/ETTh2.py
@@ -0,0 +1,153 @@
+import os
+import sys
+from easydict import EasyDict
+sys.path.append(os.path.abspath(__file__ + '/../../..'))
+from basicts.metrics import masked_mae, masked_mse, masked_mape, masked_rmse
+from basicts.data import TimeSeriesForecastingDataset
+from basicts.runners import SimpleTimeSeriesForecastingRunner
+from basicts.scaler import ZScoreScaler
+from basicts.utils import get_regular_settings
+
+from .arch import SOFTS
+
+############################## Hot Parameters ##############################
+# Dataset & Metrics configuration
+DATA_NAME = 'ETTh2'  # Dataset name
+regular_settings = get_regular_settings(DATA_NAME)
+# INPUT_LEN = regular_settings['INPUT_LEN']  # Length of input sequence
+INPUT_LEN = 192 # better performance
+OUTPUT_LEN = regular_settings['OUTPUT_LEN']  # Length of output sequence
+TRAIN_VAL_TEST_RATIO = regular_settings['TRAIN_VAL_TEST_RATIO']  # Train/Validation/Test split ratios
+NORM_EACH_CHANNEL = regular_settings['NORM_EACH_CHANNEL'] # Whether to normalize each channel of the data
+RESCALE = regular_settings['RESCALE'] # Whether to rescale the data
+NULL_VAL = regular_settings['NULL_VAL'] # Null value in the data
+# Model architecture and parameters
+MODEL_ARCH = SOFTS
+NUM_NODES = 7
+MODEL_PARAM = {
+    "enc_in": NUM_NODES,                        # num nodes
+    "dec_in": NUM_NODES,
+    "c_out": NUM_NODES,
+    "seq_len": INPUT_LEN,
+    "pred_len": OUTPUT_LEN,         # prediction sequence length
+    "e_layers": 2,                              # num of encoder layers
+    "d_model": 128,
+    "d_core": 64,
+    "d_ff": 128,
+    "dropout": 0.0,
+    "use_norm" : True,
+    "activation": "gelu",
+    "num_time_features": 4,                     # number of used time features
+    "time_of_day_size": 24,
+    "day_of_week_size": 7,
+    "day_of_month_size": 31,
+    "day_of_year_size": 366
+    }
+NUM_EPOCHS = 20
+
+############################## General Configuration ##############################
+CFG = EasyDict()
+# General settings
+CFG.DESCRIPTION = 'An Example Config'
+CFG.GPU_NUM = 1 # Number of GPUs to use (0 for CPU mode)
+# Runner
+CFG.RUNNER = SimpleTimeSeriesForecastingRunner
+
+CFG.ENV = EasyDict() # Environment settings. Default: None
+CFG.ENV.SEED = 2024 # Random seed. Default: None
+
+############################## Dataset Configuration ##############################
+CFG.DATASET = EasyDict()
+# Dataset settings
+CFG.DATASET.NAME = DATA_NAME
+CFG.DATASET.TYPE = TimeSeriesForecastingDataset
+CFG.DATASET.PARAM = EasyDict({
+    'dataset_name': DATA_NAME,
+    'train_val_test_ratio': TRAIN_VAL_TEST_RATIO,
+    'input_len': INPUT_LEN,
+    'output_len': OUTPUT_LEN,
+    # 'mode' is automatically set by the runner
+})
+
+############################## Scaler Configuration ##############################
+CFG.SCALER = EasyDict()
+# Scaler settings
+CFG.SCALER.TYPE = ZScoreScaler # Scaler class
+CFG.SCALER.PARAM = EasyDict({
+    'dataset_name': DATA_NAME,
+    'train_ratio': TRAIN_VAL_TEST_RATIO[0],
+    'norm_each_channel': NORM_EACH_CHANNEL,
+    'rescale': RESCALE,
+})
+
+############################## Model Configuration ##############################
+CFG.MODEL = EasyDict()
+# Model settings
+CFG.MODEL.NAME = MODEL_ARCH.__name__
+CFG.MODEL.ARCH = MODEL_ARCH
+CFG.MODEL.PARAM = MODEL_PARAM
+CFG.MODEL.FORWARD_FEATURES = [0, 1, 2, 3, 4]
+CFG.MODEL.TARGET_FEATURES = [0]
+
+############################## Metrics Configuration ##############################
+
+CFG.METRICS = EasyDict()
+# Metrics settings
+CFG.METRICS.FUNCS = EasyDict({
+                                'MAE': masked_mae,
+                                'MSE': masked_mse,
+                                'RMSE': masked_rmse,
+                                'MAPE': masked_mape
+                            })
+CFG.METRICS.TARGET = 'MAE'
+CFG.METRICS.NULL_VAL = NULL_VAL
+
+############################## Training Configuration ##############################
+CFG.TRAIN = EasyDict()
+CFG.TRAIN.NUM_EPOCHS = NUM_EPOCHS
+CFG.TRAIN.CKPT_SAVE_DIR = os.path.join(
+    'checkpoints',
+    MODEL_ARCH.__name__,
+    '_'.join([DATA_NAME, str(CFG.TRAIN.NUM_EPOCHS), str(INPUT_LEN), str(OUTPUT_LEN)])
+)
+CFG.TRAIN.LOSS = masked_mse
+# Optimizer settings
+CFG.TRAIN.OPTIM = EasyDict()
+CFG.TRAIN.OPTIM.TYPE = "Adam"
+CFG.TRAIN.OPTIM.PARAM = {
+    "lr": 0.0003,
+}
+# Learning rate scheduler settings
+CFG.TRAIN.LR_SCHEDULER = EasyDict()
+CFG.TRAIN.LR_SCHEDULER.TYPE = "MultiStepLR"
+CFG.TRAIN.LR_SCHEDULER.PARAM = {
+    "milestones": [1, 25, 50],
+    "gamma": 0.5
+}
+CFG.TRAIN.CLIP_GRAD_PARAM = {
+    'max_norm': 5.0
+}
+# Train data loader settings
+CFG.TRAIN.DATA = EasyDict()
+CFG.TRAIN.DATA.BATCH_SIZE = 32
+CFG.TRAIN.DATA.SHUFFLE = True
+CFG.TRAIN.EARLY_STOPPING_PATIENCE = 10
+
+############################## Validation Configuration ##############################
+CFG.VAL = EasyDict()
+CFG.VAL.INTERVAL = 1
+CFG.VAL.DATA = EasyDict()
+CFG.VAL.DATA.BATCH_SIZE = 64
+
+############################## Test Configuration ##############################
+CFG.TEST = EasyDict()
+CFG.TEST.INTERVAL = 1
+CFG.TEST.DATA = EasyDict()
+CFG.TEST.DATA.BATCH_SIZE = 64
+
+############################## Evaluation Configuration ##############################
+
+CFG.EVAL = EasyDict()
+
+# Evaluation parameters
+CFG.EVAL.USE_GPU = True # Whether to use GPU for evaluation. Default: True
diff --git a/baselines/SOFTS/Weather.py b/baselines/SOFTS/Weather.py
@@ -14,7 +14,7 @@
 # Dataset & Metrics configuration
 DATA_NAME = 'Weather'  # Dataset name
 regular_settings = get_regular_settings(DATA_NAME)
-INPUT_LEN = regular_settings['INPUT_LEN']  # Length of input sequence
+INPUT_LEN = regular_settings['INPUT_LEN']  # 336, better performance
 OUTPUT_LEN = regular_settings['OUTPUT_LEN']  # Length of output sequence
 TRAIN_VAL_TEST_RATIO = regular_settings['TRAIN_VAL_TEST_RATIO']  # Train/Validation/Test split ratios
 NORM_EACH_CHANNEL = regular_settings['NORM_EACH_CHANNEL'] # Whether to normalize each channel of the data

diff --git a/baselines/SOFTS/arch/softs_arch.py b/baselines/SOFTS/arch/softs_arch.py
@@ -54,7 +54,7 @@ class SOFTS(nn.Module):
     '''
     Paper: SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
     Official Code: https://github.com/Secilia-Cxy/SOFTS
-    Link: https://xiucheng.org/assets/pdfs/nips24-sumba.pdf
+    Link: https://arxiv.org/pdf/2404.14197
     Venue: NeurIPS 2024
     Task: Long-term Time Series Forecasting
     '''
@@ -119,18 +119,11 @@ def forward(self, history_data: torch.Tensor, future_data: torch.Tensor, batch_s
             torch.Tensor: outputs with shape [B, L2, N, 1]
         """
 
-        # change MinuteOfDay to MinuteOfHour
+        # change TimeOfDay to MinuteOfHour
         history_data[..., 1] = history_data[..., 1] * self.time_of_day_size // (self.time_of_day_size / 24) / 23.0
         x_enc, x_mark_enc, x_dec, x_mark_dec = data_transformation_4_xformer(history_data=history_data,
                                                                              future_data=future_data,
                                                                              start_token_len=0)
         #print(x_mark_enc.shape, x_mark_dec.shape)
         prediction = self.forward_xformer(x_enc=x_enc, x_mark_enc=x_mark_enc)
         return prediction.unsqueeze(-1)
-
-
-
-
-
-
-