From c2940f25e8aedfa7b79bac69027e93491b337831 Mon Sep 17 00:00:00 2001 From: Zezhi Shao <864453277@qq.com> Date: Wed, 27 Nov 2024 10:29:52 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=F0=9F=8E=B8=20add=20a=20LTSF=20baselin?= =?UTF-8?q?e=20SOFTS=20(added=20by=20@superarthurlx)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 1 + README_CN.md | 1 + baselines/SOFTS/ETTh1.py | 149 ++++++++++++++++++++++++++++ baselines/SOFTS/ETTh2.py | 153 +++++++++++++++++++++++++++++ baselines/SOFTS/Weather.py | 2 +- baselines/SOFTS/arch/softs_arch.py | 11 +-- 6 files changed, 307 insertions(+), 10 deletions(-) create mode 100644 baselines/SOFTS/ETTh1.py create mode 100644 baselines/SOFTS/ETTh2.py diff --git a/README.md b/README.md index 2a62337e..e081a641 100644 --- 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 index ae009a5a..8aa5864e 100644 --- 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 new file mode 100644 index 00000000..d53cf1bf --- /dev/null +++ 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 new file mode 100644 index 00000000..67234738 --- /dev/null +++ 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 index 504ce825..597756c6 100644 --- 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 index 1cb356b4..fe1c9eac 100644 --- 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 ''' @@ -119,7 +119,7 @@ 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, @@ -127,10 +127,3 @@ def forward(self, history_data: torch.Tensor, future_data: torch.Tensor, batch_s #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) - - - - - - -