Mirror ZHangang1/MambaClimateNet from ModelScope

.gitattributes

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+**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.gguf* filter=lfs diff=lfs merge=lfs -text
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+*.llamafile* filter=lfs diff=lfs merge=lfs -text
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+*.mlmodel filter=lfs diff=lfs merge=lfs -text
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+
+figure/结果可视化图.png filter=lfs diff=lfs merge=lfs -text
+
+结果可视化图.png filter=lfs diff=lfs merge=lfs -text
+
+figure/global.png filter=lfs diff=lfs merge=lfs -text
+figure/china.png filter=lfs diff=lfs merge=lfs -text
+figure/MambaCliMateNet.png filter=lfs diff=lfs merge=lfs -text

configuration.json

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+{"framework":"Pytorch","task":"other"}

data_provider/data_factory.py

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+from data_provider.data_loader import Dataset_ETT_hour, Dataset_ETT_minute, Dataset_Custom, Dataset_Solar, Dataset_PEMS, \
+    Dataset_Pred
+from torch.utils.data import DataLoader
+
+data_dict = {
+    'ETTh1': Dataset_ETT_hour,
+    'ETTh2': Dataset_ETT_hour,
+    'ETTm1': Dataset_ETT_minute,
+    'ETTm2': Dataset_ETT_minute,
+    'Solar': Dataset_Solar,
+    'PEMS': Dataset_PEMS,
+    'custom': Dataset_Custom,
+}
+
+
+def data_provider(args, flag):
+    Data = data_dict[args.data]
+    timeenc = 0 if args.embed != 'timeF' else 1
+
+    if flag == 'test':
+        shuffle_flag = False
+        drop_last = True
+        batch_size = 1  # bsz=1 for evaluation
+        freq = args.freq
+    elif flag == 'pred':
+        shuffle_flag = False
+        drop_last = False
+        batch_size = 1
+        freq = args.freq
+        Data = Dataset_Pred
+    else:
+        shuffle_flag = True
+        drop_last = True
+        batch_size = args.batch_size  # bsz for train and valid
+        freq = args.freq
+
+    data_set = Data(
+        root_path=args.root_path,
+        data_path=args.data_path,
+        flag=flag,
+        size=[args.seq_len, args.label_len, args.pred_len],
+        features=args.features,
+        target=args.target,
+        timeenc=timeenc,
+        freq=freq,
+    )
+    print(flag, len(data_set))
+    data_loader = DataLoader(
+        data_set,
+        batch_size=batch_size,
+        shuffle=shuffle_flag,
+        num_workers=args.num_workers,
+        drop_last=drop_last)
+    return data_set, data_loader

data_provider/data_loader.py

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+import os
+import numpy as np
+import pandas as pd
+import torch
+from torch.utils.data import Dataset, DataLoader
+from sklearn.preprocessing import StandardScaler
+from utils.timefeatures import time_features
+import warnings
+
+warnings.filterwarnings('ignore')
+
+
+class Dataset_ETT_hour(Dataset):
+    def __init__(self, root_path, flag='train', size=None,
+                 features='S', data_path='ETTh1.csv',
+                 target='OT', scale=True, timeenc=0, freq='h'):
+        # size [seq_len, label_len, pred_len]
+        # info
+        if size == None:
+            self.seq_len = 24 * 4 * 4
+            self.label_len = 24 * 4
+            self.pred_len = 24 * 4
+        else:
+            self.seq_len = size[0]
+            self.label_len = size[1]
+            self.pred_len = size[2]
+        # init
+        assert flag in ['train', 'test', 'val']
+        type_map = {'train': 0, 'val': 1, 'test': 2}
+        self.set_type = type_map[flag]
+
+        self.features = features
+        self.target = target
+        self.scale = scale
+        self.timeenc = timeenc
+        self.freq = freq
+
+        self.root_path = root_path
+        self.data_path = data_path
+        self.__read_data__()
+
+    def __read_data__(self):
+        self.scaler = StandardScaler()
+        df_raw = pd.read_csv(os.path.join(self.root_path,
+                                          self.data_path))
+
+        border1s = [0, 12 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len]
+        border2s = [12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24]
+        border1 = border1s[self.set_type]
+        border2 = border2s[self.set_type]
+
+        if self.features == 'M' or self.features == 'MS':
+            cols_data = df_raw.columns[1:]
+            df_data = df_raw[cols_data]
+        elif self.features == 'S':
+            df_data = df_raw[[self.target]]
+
+        if self.scale:
+            train_data = df_data[border1s[0]:border2s[0]]
+            self.scaler.fit(train_data.values)
+            data = self.scaler.transform(df_data.values)
+        else:
+            data = df_data.values
+
+        df_stamp = df_raw[['date']][border1:border2]
+        df_stamp['date'] = pd.to_datetime(df_stamp.date)
+        if self.timeenc == 0:
+            df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1)
+            df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1)
+            df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1)
+            df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1)
+            data_stamp = df_stamp.drop(['date'], 1).values
+        elif self.timeenc == 1:
+            data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)
+            data_stamp = data_stamp.transpose(1, 0)
+
+        self.data_x = data[border1:border2]
+        self.data_y = data[border1:border2]
+        self.data_stamp = data_stamp
+
+    def __getitem__(self, index):
+        s_begin = index
+        s_end = s_begin + self.seq_len
+        r_begin = s_end - self.label_len
+        r_end = r_begin + self.label_len + self.pred_len
+
+        seq_x = self.data_x[s_begin:s_end]
+        seq_y = self.data_y[r_begin:r_end]
+        seq_x_mark = self.data_stamp[s_begin:s_end]
+        seq_y_mark = self.data_stamp[r_begin:r_end]
+
+        return seq_x, seq_y, seq_x_mark, seq_y_mark
+
+    def __len__(self):
+        return len(self.data_x) - self.seq_len - self.pred_len + 1
+
+    def inverse_transform(self, data):
+        return self.scaler.inverse_transform(data)
+
+
+class Dataset_ETT_minute(Dataset):
+    def __init__(self, root_path, flag='train', size=None,
+                 features='S', data_path='ETTm1.csv',
+                 target='OT', scale=True, timeenc=0, freq='t'):
+        # size [seq_len, label_len, pred_len]
+        # info
+        if size == None:
+            self.seq_len = 24 * 4 * 4
+            self.label_len = 24 * 4
+            self.pred_len = 24 * 4
+        else:
+            self.seq_len = size[0]
+            self.label_len = size[1]
+            self.pred_len = size[2]
+        # init
+        assert flag in ['train', 'test', 'val']
+        type_map = {'train': 0, 'val': 1, 'test': 2}
+        self.set_type = type_map[flag]
+
+        self.features = features
+        self.target = target
+        self.scale = scale
+        self.timeenc = timeenc
+        self.freq = freq
+
+        self.root_path = root_path
+        self.data_path = data_path
+        self.__read_data__()
+
+    def __read_data__(self):
+        self.scaler = StandardScaler()
+        df_raw = pd.read_csv(os.path.join(self.root_path,
+                                          self.data_path))
+
+        border1s = [0, 12 * 30 * 24 * 4 - self.seq_len, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len]
+        border2s = [12 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4]
+        border1 = border1s[self.set_type]
+        border2 = border2s[self.set_type]
+
+        if self.features == 'M' or self.features == 'MS':
+            cols_data = df_raw.columns[1:]
+            df_data = df_raw[cols_data]
+        elif self.features == 'S':
+            df_data = df_raw[[self.target]]
+
+        if self.scale:
+            train_data = df_data[border1s[0]:border2s[0]]
+            self.scaler.fit(train_data.values)
+            data = self.scaler.transform(df_data.values)
+        else:
+            data = df_data.values
+
+        df_stamp = df_raw[['date']][border1:border2]
+        df_stamp['date'] = pd.to_datetime(df_stamp.date)
+        if self.timeenc == 0:
+            df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1)
+            df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1)
+            df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1)
+            df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1)
+            df_stamp['minute'] = df_stamp.date.apply(lambda row: row.minute, 1)
+            df_stamp['minute'] = df_stamp.minute.map(lambda x: x // 15)
+            data_stamp = df_stamp.drop(['date'], 1).values
+        elif self.timeenc == 1:
+            data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)
+            data_stamp = data_stamp.transpose(1, 0)
+
+        self.data_x = data[border1:border2]
+        self.data_y = data[border1:border2]
+        self.data_stamp = data_stamp
+
+    def __getitem__(self, index):
+        s_begin = index
+        s_end = s_begin + self.seq_len
+        r_begin = s_end - self.label_len
+        r_end = r_begin + self.label_len + self.pred_len
+
+        seq_x = self.data_x[s_begin:s_end]
+        seq_y = self.data_y[r_begin:r_end]
+        seq_x_mark = self.data_stamp[s_begin:s_end]
+        seq_y_mark = self.data_stamp[r_begin:r_end]
+
+        return seq_x, seq_y, seq_x_mark, seq_y_mark
+
+    def __len__(self):
+        return len(self.data_x) - self.seq_len - self.pred_len + 1
+
+    def inverse_transform(self, data):
+        return self.scaler.inverse_transform(data)
+
+
+class Dataset_Custom(Dataset):
+    def __init__(self, root_path, flag='train', size=None,
+                 features='S', data_path='ETTh1.csv',
+                 target='OT', scale=True, timeenc=0, freq='h'):
+        # size [seq_len, label_len, pred_len]
+        # info
+        if size == None:
+            self.seq_len = 24 * 4 * 4
+            self.label_len = 24 * 4
+            self.pred_len = 24 * 4
+        else:
+            self.seq_len = size[0]
+            self.label_len = size[1]
+            self.pred_len = size[2]
+        # init
+        assert flag in ['train', 'test', 'val']
+        type_map = {'train': 0, 'val': 1, 'test': 2}
+        self.set_type = type_map[flag]
+
+        self.features = features
+        self.target = target
+        self.scale = scale
+        self.timeenc = timeenc
+        self.freq = freq
+
+        self.root_path = root_path
+        self.data_path = data_path
+        self.__read_data__()
+
+    def __read_data__(self):
+        self.scaler = StandardScaler()
+        df_raw = pd.read_csv(os.path.join(self.root_path,
+                                          self.data_path))
+
+        '''
+        df_raw.columns: ['date', ...(other features), target feature]
+        '''
+        cols = list(df_raw.columns)
+        cols.remove(self.target)
+        cols.remove('date')
+        df_raw = df_raw[['date'] + cols + [self.target]]
+        num_train = int(len(df_raw) * 0.7)
+        num_test = int(len(df_raw) * 0.2)
+        num_vali = len(df_raw) - num_train - num_test
+        border1s = [0, num_train - self.seq_len, len(df_raw) - num_test - self.seq_len]
+        border2s = [num_train, num_train + num_vali, len(df_raw)]
+        border1 = border1s[self.set_type]
+        border2 = border2s[self.set_type]
+
+        if self.features == 'M' or self.features == 'MS':
+            cols_data = df_raw.columns[1:]
+            df_data = df_raw[cols_data]
+        elif self.features == 'S':
+            df_data = df_raw[[self.target]]
+
+        if self.scale:
+            train_data = df_data[border1s[0]:border2s[0]]
+            self.scaler.fit(train_data.values)
+            data = self.scaler.transform(df_data.values)
+        else:
+            data = df_data.values
+
+        df_stamp = df_raw[['date']][border1:border2]
+        df_stamp['date'] = pd.to_datetime(df_stamp.date)
+        if self.timeenc == 0:
+            df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1)
+            df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1)
+            df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1)
+            df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1)
+            data_stamp = df_stamp.drop(['date'], 1).values
+        elif self.timeenc == 1:
+            data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)
+            data_stamp = data_stamp.transpose(1, 0)
+
+        self.data_x = data[border1:border2]
+        self.data_y = data[border1:border2]
+        self.data_stamp = data_stamp
+
+    def __getitem__(self, index):
+        s_begin = index
+        s_end = s_begin + self.seq_len
+        r_begin = s_end - self.label_len
+        r_end = r_begin + self.label_len + self.pred_len
+
+        seq_x = self.data_x[s_begin:s_end]
+        seq_y = self.data_y[r_begin:r_end]
+        seq_x_mark = self.data_stamp[s_begin:s_end]
+        seq_y_mark = self.data_stamp[r_begin:r_end]
+
+        return seq_x, seq_y, seq_x_mark, seq_y_mark
+
+    def __len__(self):
+        return len(self.data_x) - self.seq_len - self.pred_len + 1
+
+    def inverse_transform(self, data):
+        return self.scaler.inverse_transform(data)
+
+
+class Dataset_PEMS(Dataset):
+    def __init__(self, root_path, flag='train', size=None,
+                 features='S', data_path='ETTh1.csv',
+                 target='OT', scale=True, timeenc=0, freq='h'):
+        # size [seq_len, label_len, pred_len]
+        # info
+        self.seq_len = size[0]
+        self.label_len = size[1]
+        self.pred_len = size[2]
+        # init
+        assert flag in ['train', 'test', 'val']
+        type_map = {'train': 0, 'val': 1, 'test': 2}
+        self.set_type = type_map[flag]
+
+        self.features = features
+        self.target = target
+        self.scale = scale
+        self.timeenc = timeenc
+        self.freq = freq
+
+        self.root_path = root_path
+        self.data_path = data_path
+        self.__read_data__()
+
+    def __read_data__(self):
+        self.scaler = StandardScaler()
+        data_file = os.path.join(self.root_path, self.data_path)
+        data = np.load(data_file, allow_pickle=True)
+        data = data['data'][:, :, 0]
+
+        train_ratio = 0.6
+        valid_ratio = 0.2
+        train_data = data[:int(train_ratio * len(data))]
+        valid_data = data[int(train_ratio * len(data)): int((train_ratio + valid_ratio) * len(data))]
+        test_data = data[int((train_ratio + valid_ratio) * len(data)):]
+        total_data = [train_data, valid_data, test_data]
+        data = total_data[self.set_type]
+
+        if self.scale:
+            self.scaler.fit(train_data)
+            data = self.scaler.transform(data)
+
+        df = pd.DataFrame(data)
+        df = df.fillna(method='ffill', limit=len(df)).fillna(method='bfill', limit=len(df)).values
+
+        self.data_x = df
+        self.data_y = df
+
+    def __getitem__(self, index):
+        s_begin = index
+        s_end = s_begin + self.seq_len
+        r_begin = s_end - self.label_len
+        r_end = r_begin + self.label_len + self.pred_len
+
+        seq_x = self.data_x[s_begin:s_end]
+        seq_y = self.data_y[r_begin:r_end]
+        seq_x_mark = torch.zeros((seq_x.shape[0], 1))
+        seq_y_mark = torch.zeros((seq_x.shape[0], 1))
+
+        return seq_x, seq_y, seq_x_mark, seq_y_mark
+
+    def __len__(self):
+        return len(self.data_x) - self.seq_len - self.pred_len + 1
+
+    def inverse_transform(self, data):
+        return self.scaler.inverse_transform(data)
+
+
+class Dataset_Solar(Dataset):
+    def __init__(self, root_path, flag='train', size=None,
+                 features='S', data_path='ETTh1.csv',
+                 target='OT', scale=True, timeenc=0, freq='h'):
+        # size [seq_len, label_len, pred_len]
+        # info
+        self.seq_len = size[0]
+        self.label_len = size[1]
+        self.pred_len = size[2]
+        # init
+        assert flag in ['train', 'test', 'val']
+        type_map = {'train': 0, 'val': 1, 'test': 2}
+        self.set_type = type_map[flag]
+
+        self.features = features
+        self.target = target
+        self.scale = scale
+        self.timeenc = timeenc
+        self.freq = freq
+
+        self.root_path = root_path
+        self.data_path = data_path
+        self.__read_data__()
+
+    def __read_data__(self):
+        self.scaler = StandardScaler()
+        df_raw = []
+        with open(os.path.join(self.root_path, self.data_path), "r", encoding='utf-8') as f:
+            for line in f.readlines():
+                line = line.strip('\n').split(',')
+                data_line = np.stack([float(i) for i in line])
+                df_raw.append(data_line)
+        df_raw = np.stack(df_raw, 0)
+        df_raw = pd.DataFrame(df_raw)
+
+        num_train = int(len(df_raw) * 0.7)
+        num_test = int(len(df_raw) * 0.2)
+        num_valid = int(len(df_raw) * 0.1)
+        border1s = [0, num_train - self.seq_len, len(df_raw) - num_test - self.seq_len]
+        border2s = [num_train, num_train + num_valid, len(df_raw)]
+        border1 = border1s[self.set_type]
+        border2 = border2s[self.set_type]
+
+        df_data = df_raw.values
+
+        if self.scale:
+            train_data = df_data[border1s[0]:border2s[0]]
+            self.scaler.fit(train_data)
+            data = self.scaler.transform(df_data)
+        else:
+            data = df_data
+
+        self.data_x = data[border1:border2]
+        self.data_y = data[border1:border2]
+
+    def __getitem__(self, index):
+        s_begin = index
+        s_end = s_begin + self.seq_len
+        r_begin = s_end - self.label_len
+        r_end = r_begin + self.label_len + self.pred_len
+
+        seq_x = self.data_x[s_begin:s_end]
+        seq_y = self.data_y[r_begin:r_end]
+        seq_x_mark = torch.zeros((seq_x.shape[0], 1))
+        seq_y_mark = torch.zeros((seq_x.shape[0], 1))
+
+        return seq_x, seq_y, seq_x_mark, seq_y_mark
+
+    def __len__(self):
+        return len(self.data_x) - self.seq_len - self.pred_len + 1
+
+    def inverse_transform(self, data):
+        return self.scaler.inverse_transform(data)
+
+
+class Dataset_Pred(Dataset):
+    def __init__(self, root_path, flag='pred', size=None,
+                 features='S', data_path='ETTh1.csv',
+                 target='OT', scale=True, inverse=False, timeenc=0, freq='15min', cols=None):
+        # size [seq_len, label_len, pred_len]
+        # info
+        if size == None:
+            self.seq_len = 24 * 4 * 4
+            self.label_len = 24 * 4
+            self.pred_len = 24 * 4
+        else:
+            self.seq_len = size[0]
+            self.label_len = size[1]
+            self.pred_len = size[2]
+        # init
+        assert flag in ['pred']
+
+        self.features = features
+        self.target = target
+        self.scale = scale
+        self.inverse = inverse
+        self.timeenc = timeenc
+        self.freq = freq
+        self.cols = cols
+        self.root_path = root_path
+        self.data_path = data_path
+        self.__read_data__()
+
+    def __read_data__(self):
+        self.scaler = StandardScaler()
+        df_raw = pd.read_csv(os.path.join(self.root_path,
+                                          self.data_path))
+        '''
+        df_raw.columns: ['date', ...(other features), target feature]
+        '''
+        if self.cols:
+            cols = self.cols.copy()
+            cols.remove(self.target)
+        else:
+            cols = list(df_raw.columns)
+            cols.remove(self.target)
+            cols.remove('date')
+        df_raw = df_raw[['date'] + cols + [self.target]]
+        border1 = len(df_raw) - self.seq_len
+        border2 = len(df_raw)
+
+        if self.features == 'M' or self.features == 'MS':
+            cols_data = df_raw.columns[1:]
+            df_data = df_raw[cols_data]
+        elif self.features == 'S':
+            df_data = df_raw[[self.target]]
+
+        if self.scale:
+            self.scaler.fit(df_data.values)
+            data = self.scaler.transform(df_data.values)
+        else:
+            data = df_data.values
+
+        tmp_stamp = df_raw[['date']][border1:border2]
+        tmp_stamp['date'] = pd.to_datetime(tmp_stamp.date)
+        pred_dates = pd.date_range(tmp_stamp.date.values[-1], periods=self.pred_len + 1, freq=self.freq)
+
+        df_stamp = pd.DataFrame(columns=['date'])
+        df_stamp.date = list(tmp_stamp.date.values) + list(pred_dates[1:])
+        if self.timeenc == 0:
+            df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1)
+            df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1)
+            df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1)
+            df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1)
+            df_stamp['minute'] = df_stamp.date.apply(lambda row: row.minute, 1)
+            df_stamp['minute'] = df_stamp.minute.map(lambda x: x // 15)
+            data_stamp = df_stamp.drop(['date'], 1).values
+        elif self.timeenc == 1:
+            data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)
+            data_stamp = data_stamp.transpose(1, 0)
+
+        self.data_x = data[border1:border2]
+        if self.inverse:
+            self.data_y = df_data.values[border1:border2]
+        else:
+            self.data_y = data[border1:border2]
+        self.data_stamp = data_stamp
+
+    def __getitem__(self, index):
+        s_begin = index
+        s_end = s_begin + self.seq_len
+        r_begin = s_end - self.label_len
+        r_end = r_begin + self.label_len + self.pred_len
+
+        seq_x = self.data_x[s_begin:s_end]
+        if self.inverse:
+            seq_y = self.data_x[r_begin:r_begin + self.label_len]
+        else:
+            seq_y = self.data_y[r_begin:r_begin + self.label_len]
+        seq_x_mark = self.data_stamp[s_begin:s_end]
+        seq_y_mark = self.data_stamp[r_begin:r_end]
+
+        return seq_x, seq_y, seq_x_mark, seq_y_mark
+
+    def __len__(self):
+        return len(self.data_x) - self.seq_len + 1
+
+    def inverse_transform(self, data):
+        return self.scaler.inverse_transform(data)

dataset/China.csv

@@ -0,0 +1,1462 @@
+date,temp_max,temp_min,temp_mean,precip,windspeed_max,windgusts_max,sunshine_duration
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dataset/s.csv

@@ -0,0 +1 @@
+undefined

experiments/exp_basic.py

@@ -0,0 +1,40 @@
+import os
+import torch
+from model import MambaClimateNet
+
+
+class Exp_Basic(object):
+    def __init__(self, args):
+        self.args = args
+        self.model_dict = {
+            'MambaClimateNet': MambaClimateNet,
+        }
+        self.device = self._acquire_device()
+        self.model = self._build_model().to(self.device)
+
+    def _build_model(self):
+        raise NotImplementedError
+        return None
+
+    def _acquire_device(self):
+        if self.args.use_gpu:
+            os.environ["CUDA_VISIBLE_DEVICES"] = str(
+                self.args.gpu) if not self.args.use_multi_gpu else self.args.devices
+            device = torch.device('cuda:{}'.format(self.args.gpu))
+            print('Use GPU: cuda:{}'.format(self.args.gpu))
+        else:
+            device = torch.device('cpu')
+            print('Use CPU')
+        return device
+
+    def _get_data(self):
+        pass
+
+    def vali(self):
+        pass
+
+    def train(self):
+        pass
+
+    def test(self):
+        pass

experiments/exp_long_term_forecasting.py

@@ -0,0 +1,351 @@
+import random
+
+from data_provider.data_factory import data_provider
+from experiments.exp_basic import Exp_Basic
+from utils.tools import EarlyStopping, adjust_learning_rate, visual
+from utils.metrics import metric
+import torch
+import torch.nn as nn
+from torch import optim
+import os
+import time
+import warnings
+import numpy as np
+import csv
+
+warnings.filterwarnings('ignore')
+
+
+class Exp_Long_Term_Forecast(Exp_Basic):
+    def __init__(self, args):
+        super(Exp_Long_Term_Forecast, self).__init__(args)
+
+    def _build_model(self):
+        model = self.model_dict[self.args.model].Model(self.args).float()
+
+        if self.args.use_multi_gpu and self.args.use_gpu:
+            model = nn.DataParallel(model, device_ids=self.args.device_ids)
+        return model
+
+    def _get_data(self, flag):
+        data_set, data_loader = data_provider(self.args, flag)
+        return data_set, data_loader
+
+    def _select_optimizer(self):
+        model_optim = optim.Adam(self.model.parameters(), lr=self.args.learning_rate)
+        return model_optim
+
+    def _select_criterion(self):
+        criterion = nn.MSELoss()
+        return criterion
+
+    def vali(self, vali_data, vali_loader, criterion):
+        total_loss = []
+        self.model.eval()
+        with torch.no_grad():
+            for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(vali_loader):
+                batch_x = batch_x.float().to(self.device)
+                batch_y = batch_y.float()
+
+                if 'PEMS' in self.args.data or 'Solar' in self.args.data:
+                    batch_x_mark = None
+                    batch_y_mark = None
+                else:
+                    batch_x_mark = batch_x_mark.float().to(self.device)
+                    batch_y_mark = batch_y_mark.float().to(self.device)
+
+                # decoder input
+                dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
+                dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
+                # encoder - decoder
+                if self.args.use_amp:
+                    with torch.cuda.amp.autocast():
+                        if self.args.output_attention:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                        else:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                else:
+                    if self.args.output_attention:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                    else:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                f_dim = -1 if self.args.features == 'MS' else 0
+                outputs = outputs[:, -self.args.pred_len:, f_dim:]
+                batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
+
+                pred = outputs.detach().cpu()
+                true = batch_y.detach().cpu()
+
+                loss = criterion(pred, true)
+
+                total_loss.append(loss)
+        total_loss = np.average(total_loss)
+        self.model.train()
+        return total_loss
+
+    def train(self, setting):
+        train_data, train_loader = self._get_data(flag='train')
+        vali_data, vali_loader = self._get_data(flag='val')
+        test_data, test_loader = self._get_data(flag='test')
+
+        path = os.path.join(self.args.checkpoints, setting)
+        if not os.path.exists(path):
+            os.makedirs(path)
+
+        time_now = time.time()
+
+        train_steps = len(train_loader)
+        early_stopping = EarlyStopping(patience=self.args.patience, verbose=True)
+
+        model_optim = self._select_optimizer()
+        criterion = self._select_criterion()
+
+        if self.args.use_amp:
+            scaler = torch.cuda.amp.GradScaler()
+
+        for epoch in range(self.args.train_epochs):
+            iter_count = 0
+            train_loss = []
+
+            self.model.train()
+            epoch_time = time.time()
+            for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(train_loader):
+                iter_count += 1
+                model_optim.zero_grad()
+                batch_x = batch_x.float().to(self.device)
+                batch_y = batch_y.float().to(self.device)
+                if 'PEMS' in self.args.data or 'Solar' in self.args.data:
+                    batch_x_mark = None
+                    batch_y_mark = None
+                else:
+                    batch_x_mark = batch_x_mark.float().to(self.device)
+                    batch_y_mark = batch_y_mark.float().to(self.device)
+
+                # decoder input
+                dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
+                dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
+
+                # encoder - decoder
+                if self.args.use_amp:
+                    with torch.cuda.amp.autocast():
+                        if self.args.output_attention:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                        else:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+
+                        f_dim = -1 if self.args.features == 'MS' else 0
+                        outputs = outputs[:, -self.args.pred_len:, f_dim:]
+                        batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
+                        loss = criterion(outputs, batch_y)
+                        train_loss.append(loss.item())
+                else:
+                    if self.args.output_attention:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                    else:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+
+                    f_dim = -1 if self.args.features == 'MS' else 0
+                    outputs = outputs[:, -self.args.pred_len:, f_dim:]
+                    batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
+                    loss = criterion(outputs, batch_y)
+                    train_loss.append(loss.item())
+
+                if (i + 1) % 100 == 0:
+                    print("\titers: {0}, epoch: {1} | loss: {2:.7f}".format(i + 1, epoch + 1, loss.item()))
+                    speed = (time.time() - time_now) / iter_count
+                    # print(speed)
+                    # allocated_memory = torch.cuda.memory_allocated() / (1024 * 1024 * 1024)
+                    # cached_memory = torch.cuda.memory_cached() / (1024 * 1024 * 1024)
+                    # total = allocated_memory + cached_memory
+                    # print('allocated_memory:', allocated_memory)
+                    # print('cached_memory:', cached_memory)
+                    # print('total:', total)
+                    left_time = speed * ((self.args.train_epochs - epoch) * train_steps - i)
+                    print('\tspeed: {:.4f}s/iter; left time: {:.4f}s'.format(speed, left_time))
+                    iter_count = 0
+                    time_now = time.time()
+
+                if self.args.use_amp:
+                    scaler.scale(loss).backward()
+                    scaler.step(model_optim)
+                    scaler.update()
+                else:
+                    loss.backward()
+                    model_optim.step()
+
+            print("Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time))
+            train_loss = np.average(train_loss)
+            vali_loss = self.vali(vali_data, vali_loader, criterion)
+            test_loss = self.vali(test_data, test_loader, criterion)
+
+            print("Epoch: {0}, Steps: {1} | Train Loss: {2:.7f} Vali Loss: {3:.7f} Test Loss: {4:.7f}".format(
+                epoch + 1, train_steps, train_loss, vali_loss, test_loss))
+            early_stopping(vali_loss, self.model, path)
+            if early_stopping.early_stop:
+                print("Early stopping")
+                break
+
+            adjust_learning_rate(model_optim, epoch + 1, self.args)
+
+            # get_cka(self.args, setting, self.model, train_loader, self.device, epoch)
+
+        best_model_path = path + '/' + 'checkpoint.pth'
+        self.model.load_state_dict(torch.load(best_model_path))
+
+        return self.model
+
+    def test(self, setting, test=0):
+        test_data, test_loader = self._get_data(flag='test')
+        if test:
+            print('loading model')
+            self.model.load_state_dict(torch.load(os.path.join('./checkpoints/' + setting, 'checkpoint.pth')))
+
+        preds = []
+        trues = []
+        folder_path = './test_results/' + setting + '/'
+        if not os.path.exists(folder_path):
+            os.makedirs(folder_path)
+
+        self.model.eval()
+        with torch.no_grad():
+            for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(test_loader):
+                batch_x = batch_x.float().to(self.device)
+                batch_y = batch_y.float().to(self.device)
+                if 'PEMS' in self.args.data or 'Solar' in self.args.data:
+                    batch_x_mark = None
+                    batch_y_mark = None
+                else:
+                    batch_x_mark = batch_x_mark.float().to(self.device)
+                    batch_y_mark = batch_y_mark.float().to(self.device)
+
+                # decoder input
+                dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
+                dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
+                # encoder - decoder
+                if self.args.use_amp:
+                    with torch.cuda.amp.autocast():
+                        if self.args.output_attention:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                        else:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                else:
+                    if self.args.output_attention:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                    else:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+
+                f_dim = -1 if self.args.features == 'MS' else 0
+                outputs = outputs[:, -self.args.pred_len:, :]
+                batch_y = batch_y[:, -self.args.pred_len:, :].to(self.device)
+                outputs = outputs.detach().cpu().numpy()
+                batch_y = batch_y.detach().cpu().numpy()
+                if test_data.scale and self.args.inverse:
+                    shape = batch_y.shape
+                    if self.args.features == 'MS':
+                        outputs = np.tile(outputs, [1, 1, batch_y.shape[-1]])
+                    outputs = test_data.inverse_transform(outputs.reshape(shape[0] * shape[1], -1)).reshape(shape)
+                    batch_y = test_data.inverse_transform(batch_y.reshape(shape[0] * shape[1], -1)).reshape(shape)
+        
+                outputs = outputs[:, :, f_dim:]
+                batch_y = batch_y[:, :, f_dim:]
+                
+                pred = outputs
+                true = batch_y
+
+                preds.append(pred)
+                trues.append(true)
+                if i % 20 == 0:
+                    input = batch_x.detach().cpu().numpy()
+                    if test_data.scale and self.args.inverse:
+                        shape = input.shape
+                        input = test_data.inverse_transform(input.reshape(shape[0] * shape[1], -1)).reshape(shape)
+                    gt = np.concatenate((input[0, :, -1], true[0, :, -1]), axis=0)
+                    pd = np.concatenate((input[0, :, -1], pred[0, :, -1]), axis=0)
+                    visual(gt, pd, os.path.join(folder_path, str(i) + '.pdf'))
+
+        preds = np.array(preds)
+        trues = np.array(trues)
+        print('test shape:', preds.shape, trues.shape)
+        preds = preds.reshape(-1, preds.shape[-2], preds.shape[-1])
+        trues = trues.reshape(-1, trues.shape[-2], trues.shape[-1])
+        print('test shape:', preds.shape, trues.shape)
+
+        # result save
+        folder_path = './results/' + setting + '/'
+        if not os.path.exists(folder_path):
+            os.makedirs(folder_path)   
+        mae, mse, rmse, mape, mspe,corr = metric(preds, trues)
+    
+            
+        print('mse:{}, mae:{}'.format(mse, mae))
+        f = open("result_long_term_forecast.txt", 'a')
+        f.write(setting + "  \n")
+        f.write('mse:{}, mae:{}'.format(mse, mae))
+        f.write('\n')
+        f.write('\n')
+        f.close()
+
+        np.save(folder_path + 'metrics.npy', np.array([mae, mse, rmse, mape, mspe]))
+        np.save(folder_path + 'pred.npy', preds)
+        np.save(folder_path + 'true.npy', trues)
+
+        return
+    def get_input(self, setting):
+        test_data, test_loader = self._get_data(flag='test')
+        inputs = []
+        for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(test_loader):
+            input = batch_x.detach().cpu().numpy()
+            inputs.append((input))
+        folder_path = './results/' + setting + '/'
+        np.save(folder_path + 'input.npy', inputs)
+
+    def predict(self, setting, load=False):
+        pred_data, pred_loader = self._get_data(flag='pred')
+
+        if load:
+            path = os.path.join(self.args.checkpoints, setting)
+            best_model_path = path + '/' + 'checkpoint.pth'
+            self.model.load_state_dict(torch.load(best_model_path))
+
+        preds = []
+
+        self.model.eval()
+        with torch.no_grad():
+            for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(pred_loader):
+                batch_x = batch_x.float().to(self.device)
+                batch_y = batch_y.float()
+                batch_x_mark = batch_x_mark.float().to(self.device)
+                batch_y_mark = batch_y_mark.float().to(self.device)
+
+                # decoder input
+                dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
+                dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
+                # encoder - decoder
+                if self.args.use_amp:
+                    with torch.cuda.amp.autocast():
+                        if self.args.output_attention:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                        else:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                else:
+                    if self.args.output_attention:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                    else:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                outputs = outputs.detach().cpu().numpy()
+                if pred_data.scale and self.args.inverse:
+                    shape = outputs.shape
+                    outputs = pred_data.inverse_transform(outputs.squeeze(0)).reshape(shape)
+                preds.append(outputs)
+
+        preds = np.array(preds)
+        preds = preds.reshape(-1, preds.shape[-2], preds.shape[-1])
+
+        # result save
+        folder_path = './results/' + setting + '/'
+        if not os.path.exists(folder_path):
+            os.makedirs(folder_path)
+
+        np.save(folder_path + 'real_prediction.npy', preds)
+
+        return

experiments/exp_long_term_forecasting_partial.py

@@ -0,0 +1,397 @@
+from data_provider.data_factory import data_provider
+from experiments.exp_basic import Exp_Basic
+from utils.tools import EarlyStopping, adjust_learning_rate, visual
+from utils.metrics import metric
+import torch
+import torch.nn as nn
+from torch import optim
+import os
+import time
+import warnings
+import pdb
+import numpy as np
+import random
+
+warnings.filterwarnings('ignore')
+
+
+# train on partial variate data and test on the full variates, used for two types of experiments:
+# (1) Generalize on unseen variate (Figure 5 of our paper)
+# (2) Efficient training strategy  (Figure 8 of our paper)
+class Exp_Long_Term_Forecast_Partial(Exp_Basic):
+    def __init__(self, args):
+        super(Exp_Long_Term_Forecast_Partial, self).__init__(args)
+
+    def _build_model(self):
+        model = self.model_dict[self.args.model].Model(self.args).float()
+
+        if self.args.use_multi_gpu and self.args.use_gpu:
+            model = nn.DataParallel(model, device_ids=self.args.device_ids)
+        return model
+
+    def _get_data(self, flag):
+        data_set, data_loader = data_provider(self.args, flag)
+        return data_set, data_loader
+
+    def _select_optimizer(self):
+        model_optim = optim.Adam(self.model.parameters(), lr=self.args.learning_rate)
+        return model_optim
+
+    def _select_criterion(self):
+        criterion = nn.MSELoss()
+        return criterion
+
+    def vali(self, vali_data, vali_loader, criterion, partial_train=False):
+        total_loss = []
+        self.model.eval()
+        with torch.no_grad():
+            for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(vali_loader):
+                batch_x = batch_x.float().to(self.device)
+                batch_y = batch_y.float()
+
+                if 'PEMS' in self.args.data or 'Solar' in self.args.data:
+                    batch_x_mark = None
+                    batch_y_mark = None
+                else:
+                    batch_x_mark = batch_x_mark.float().to(self.device)
+                    batch_y_mark = batch_y_mark.float().to(self.device)
+
+                if partial_train:  # we train models with only partial variates from the dataset
+                    partial_start = self.args.partial_start_index
+                    partial_end = min(self.args.enc_in + partial_start, batch_x.shape[-1])
+                    batch_x = batch_x[:, :, partial_start:partial_end]
+                    batch_y = batch_y[:, :, partial_start:partial_end]
+
+                # decoder input
+                dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
+                dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
+                # encoder - decoder
+                if self.args.use_amp:
+                    with torch.cuda.amp.autocast():
+                        if self.args.output_attention:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                        else:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                else:
+                    if self.args.output_attention:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                    elif self.args.channel_independence:
+                        B, Tx, N = batch_x.shape
+                        _, Ty, _ = dec_inp.shape
+                        if batch_x_mark == None:
+                            outputs = self.model(batch_x.permute(0, 2, 1).reshape(B * N, Tx, 1), batch_x_mark, \
+                                                 dec_inp.permute(0, 2, 1).reshape(B * N, Ty, 1), batch_y_mark).reshape(
+                                B, N, -1).permute(0, 2, 1)
+                        else:
+                            outputs = self.model(batch_x.permute(0, 2, 1).reshape(B * N, Tx, 1),
+                                                 batch_x_mark.repeat(N, 1, 1), \
+                                                 dec_inp.permute(0, 2, 1).reshape(B * N, Ty, 1),
+                                                 batch_y_mark.repeat(N, 1, 1)) \
+                                .reshape(B, N, -1).permute(0, 2, 1)
+                    else:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                f_dim = -1 if self.args.features == 'MS' else 0
+                outputs = outputs[:, -self.args.pred_len:, f_dim:]
+                batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
+
+                pred = outputs.detach().cpu()
+                true = batch_y.detach().cpu()
+
+                loss = criterion(pred, true)
+
+                total_loss.append(loss)
+        total_loss = np.average(total_loss)
+        self.model.train()
+        return total_loss
+
+    def train(self, setting):
+        train_data, train_loader = self._get_data(flag='train')
+        vali_data, vali_loader = self._get_data(flag='val')
+        test_data, test_loader = self._get_data(flag='test')
+
+        path = os.path.join(self.args.checkpoints, setting)
+        if not os.path.exists(path):
+            os.makedirs(path)
+
+        time_now = time.time()
+
+        train_steps = len(train_loader)
+        early_stopping = EarlyStopping(patience=self.args.patience, verbose=True)
+
+        model_optim = self._select_optimizer()
+        criterion = self._select_criterion()
+
+        if self.args.use_amp:
+            scaler = torch.cuda.amp.GradScaler()
+
+        for epoch in range(self.args.train_epochs):
+            iter_count = 0
+            train_loss = []
+
+            self.model.train()
+            epoch_time = time.time()
+            for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(train_loader):
+                iter_count += 1
+                model_optim.zero_grad()
+                batch_x = batch_x.float().to(self.device)
+
+                batch_y = batch_y.float().to(self.device)
+                if 'PEMS' in self.args.data or 'Solar' in self.args.data:
+                    batch_x_mark = None
+                    batch_y_mark = None
+                else:
+                    batch_x_mark = batch_x_mark.float().to(self.device)
+                    batch_y_mark = batch_y_mark.float().to(self.device)
+
+                # Variate Generalization training: 
+                # We train with partial variates (args.enc_in < number of dataset variates)
+                # and test the obtained model directly on all variates.
+                partial_start = self.args.partial_start_index
+                partial_end = min(self.args.enc_in + partial_start, batch_x.shape[-1])
+                batch_x = batch_x[:, :, partial_start:partial_end]
+                batch_y = batch_y[:, :, partial_start:partial_end]
+                # Efficient training strategy: randomly choose part of the variates
+                # and only train the model with selected variates in each batch 
+                if self.args.efficient_training:
+                    _, _, N = batch_x.shape
+                    index = np.stack(random.sample(range(N), N))[-self.args.enc_in:]
+                    batch_x = batch_x[:, :, index]
+                    batch_y = batch_y[:, :, index]
+
+                # decoder input
+                dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
+                dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
+
+                # encoder - decoder
+                if self.args.use_amp:
+                    with torch.cuda.amp.autocast():
+                        if self.args.output_attention:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                        else:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+
+                        f_dim = -1 if self.args.features == 'MS' else 0
+                        outputs = outputs[:, -self.args.pred_len:, f_dim:]
+                        batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
+                        loss = criterion(outputs, batch_y)
+                        train_loss.append(loss.item())
+                else:
+                    if self.args.output_attention:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                    elif self.args.channel_independence:
+                        B, Tx, N = batch_x.shape
+                        _, Ty, _ = dec_inp.shape
+                        if batch_x_mark == None:
+                            outputs = self.model(batch_x.permute(0, 2, 1).reshape(B * N, Tx, 1), batch_x_mark, \
+                                                 dec_inp.permute(0, 2, 1).reshape(B * N, Ty, 1), batch_y_mark).reshape(
+                                B, N, -1).permute(0, 2, 1)
+                        else:
+                            a = batch_x.permute(0, 2, 1)
+                            b = batch_x.permute(0, 2, 1).reshape(B * N, Tx, 1)
+                            outputs = self.model(batch_x.permute(0, 2, 1).reshape(B * N, Tx, 1),
+                                                 batch_x_mark.repeat(N, 1, 1), \
+                                                 dec_inp.permute(0, 2, 1).reshape(B * N, Ty, 1),
+                                                 batch_y_mark.repeat(N, 1, 1)) \
+                                .reshape(B, N, -1).permute(0, 2, 1)
+                    else:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+
+                    f_dim = -1 if self.args.features == 'MS' else 0
+                    outputs = outputs[:, -self.args.pred_len:, f_dim:]
+                    batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
+                    loss = criterion(outputs, batch_y)
+                    train_loss.append(loss.item())
+
+                if (i + 1) % 100 == 0:
+                    print("\titers: {0}, epoch: {1} | loss: {2:.7f}".format(i + 1, epoch + 1, loss.item()))
+                    speed = (time.time() - time_now) / iter_count
+                    left_time = speed * ((self.args.train_epochs - epoch) * train_steps - i)
+                    print('\tspeed: {:.4f}s/iter; left time: {:.4f}s'.format(speed, left_time))
+                    iter_count = 0
+                    time_now = time.time()
+
+                if self.args.use_amp:
+                    scaler.scale(loss).backward()
+                    scaler.step(model_optim)
+                    scaler.update()
+                else:
+                    loss.backward()
+                    model_optim.step()
+
+            print("Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time))
+            train_loss = np.average(train_loss)
+            vali_loss = self.vali(vali_data, vali_loader, criterion, partial_train=True)
+            test_loss = self.vali(test_data, test_loader, criterion, partial_train=False)
+
+            print("Epoch: {0}, Steps: {1} | Train Loss: {2:.7f} Vali Loss: {3:.7f} Test Loss: {4:.7f}".format(
+                epoch + 1, train_steps, train_loss, vali_loss, test_loss))
+            early_stopping(vali_loss, self.model, path)
+            if early_stopping.early_stop:
+                print("Early stopping")
+                break
+
+            adjust_learning_rate(model_optim, epoch + 1, self.args)
+
+        best_model_path = path + '/' + 'checkpoint.pth'
+        self.model.load_state_dict(torch.load(best_model_path))
+
+        return self.model
+
+    def test(self, setting, test=0):
+
+        test_data, test_loader = self._get_data(flag='test')
+        if test:
+            print('loading model')
+            self.model.load_state_dict(torch.load(os.path.join('./checkpoints/' + setting, 'checkpoint.pth')))
+
+        preds = []
+        trues = []
+        folder_path = './test_results/' + setting + '/'
+        if not os.path.exists(folder_path):
+            os.makedirs(folder_path)
+
+        self.model.eval()
+        with torch.no_grad():
+            for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(test_loader):
+                # During model inference, test the obtained model directly on all variates.
+                batch_x = batch_x.float().to(self.device)
+                batch_y = batch_y.float().to(self.device)
+
+                if 'PEMS' in self.args.data or 'Solar' in self.args.data:
+                    batch_x_mark = None
+                    batch_y_mark = None
+                else:
+                    batch_x_mark = batch_x_mark.float().to(self.device)
+                    batch_y_mark = batch_y_mark.float().to(self.device)
+
+                # decoder input
+                dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
+                dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
+                # encoder - decoder
+                if self.args.use_amp:
+                    with torch.cuda.amp.autocast():
+                        if self.args.output_attention:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                        else:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                else:
+                    if self.args.output_attention:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                    elif self.args.channel_independence:  # compare the result with channel_independence
+                        B, Tx, N = batch_x.shape
+                        _, Ty, _ = dec_inp.shape
+                        if batch_x_mark == None:
+                            outputs = self.model(batch_x.permute(0, 2, 1).reshape(B * N, Tx, 1), batch_x_mark, \
+                                                 dec_inp.permute(0, 2, 1).reshape(B * N, Ty, 1), batch_y_mark).reshape(
+                                B, N, -1).permute(0, 2, 1)
+                        else:
+                            outputs = self.model(batch_x.permute(0, 2, 1).reshape(B * N, Tx, 1),
+                                                 batch_x_mark.repeat(N, 1, 1), \
+                                                 dec_inp.permute(0, 2, 1).reshape(B * N, Ty, 1),
+                                                 batch_y_mark.repeat(N, 1, 1)) \
+                                .reshape(B, N, -1).permute(0, 2, 1)
+                    else:
+                        # directly test the trained model on all variates without fine-tuning.
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+
+                f_dim = -1 if self.args.features == 'MS' else 0
+                outputs = outputs[:, -self.args.pred_len:, f_dim:]
+                batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
+                outputs = outputs.detach().cpu().numpy()
+                batch_y = batch_y.detach().cpu().numpy()
+                if test_data.scale and self.args.inverse:
+                    shape = outputs.shape
+                    outputs = test_data.inverse_transform(outputs.squeeze(0)).reshape(shape)
+                    batch_y = test_data.inverse_transform(batch_y.squeeze(0)).reshape(shape)
+
+                pred = outputs
+                true = batch_y
+
+                preds.append(pred)
+                trues.append(true)
+                if i % 20 == 0:
+                    input = batch_x.detach().cpu().numpy()
+                    if test_data.scale and self.args.inverse:
+                        shape = input.shape
+                        input = test_data.inverse_transform(input.squeeze(0)).reshape(shape)
+                    gt = np.concatenate((input[0, :, -1], true[0, :, -1]), axis=0)
+                    pd = np.concatenate((input[0, :, -1], pred[0, :, -1]), axis=0)
+                    visual(gt, pd, os.path.join(folder_path, str(i) + '.pdf'))
+
+        preds = np.array(preds)
+        trues = np.array(trues)
+        print('test shape:', preds.shape, trues.shape)
+        preds = preds.reshape(-1, preds.shape[-2], preds.shape[-1])
+        trues = trues.reshape(-1, trues.shape[-2], trues.shape[-1])
+        print('test shape:', preds.shape, trues.shape)
+
+        # result save
+        folder_path = './results/' + setting + '/'
+        if not os.path.exists(folder_path):
+            os.makedirs(folder_path)
+
+        mae, mse, rmse, mape, mspe = metric(preds, trues)
+        print('mse:{}, mae:{}'.format(mse, mae))
+        f = open("result_long_term_forecast.txt", 'a')
+        f.write(setting + "  \n")
+        f.write('mse:{}, mae:{}'.format(mse, mae))
+        f.write('\n')
+        f.write('\n')
+        f.close()
+
+        np.save(folder_path + 'metrics.npy', np.array([mae, mse, rmse, mape, mspe]))
+        np.save(folder_path + 'pred.npy', preds)
+        np.save(folder_path + 'true.npy', trues)
+
+        return
+
+    def predict(self, setting, load=False):
+        pred_data, pred_loader = self._get_data(flag='pred')
+
+        if load:
+            path = os.path.join(self.args.checkpoints, setting)
+            best_model_path = path + '/' + 'checkpoint.pth'
+            self.model.load_state_dict(torch.load(best_model_path))
+
+        preds = []
+
+        self.model.eval()
+        with torch.no_grad():
+            for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(pred_loader):
+                batch_x = batch_x.float().to(self.device)
+                batch_y = batch_y.float()
+                batch_x_mark = batch_x_mark.float().to(self.device)
+                batch_y_mark = batch_y_mark.float().to(self.device)
+
+                # decoder input
+                dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
+                dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
+                # encoder - decoder
+                if self.args.use_amp:
+                    with torch.cuda.amp.autocast():
+                        if self.args.output_attention:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                        else:
+                            outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                else:
+                    if self.args.output_attention:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
+                    else:
+                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                outputs = outputs.detach().cpu().numpy()
+                if pred_data.scale and self.args.inverse:
+                    shape = outputs.shape
+                    outputs = pred_data.inverse_transform(outputs.squeeze(0)).reshape(shape)
+                preds.append(outputs)
+
+        preds = np.array(preds)
+        preds = preds.reshape(-1, preds.shape[-2], preds.shape[-1])
+
+        # result save
+        folder_path = './results/' + setting + '/'
+        if not os.path.exists(folder_path):
+            os.makedirs(folder_path)
+
+        np.save(folder_path + 'real_prediction.npy', preds)
+
+        return

layers/Embed.py

@@ -0,0 +1,190 @@
+import torch
+import torch.nn as nn
+import math
+
+from mamba_ssm import Mamba
+class PositionalEmbedding(nn.Module):
+    def __init__(self, d_model, max_len=5000):
+        super(PositionalEmbedding, self).__init__()
+        # Compute the positional encodings once in log space.
+        pe = torch.zeros(max_len, d_model).float()
+        pe.require_grad = False
+
+        position = torch.arange(0, max_len).float().unsqueeze(1)
+        div_term = (torch.arange(0, d_model, 2).float()
+                    * -(math.log(10000.0) / d_model)).exp()
+
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+
+        pe = pe.unsqueeze(0)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        return self.pe[:, :x.size(1)]
+
+
+class TokenEmbedding(nn.Module):
+    def __init__(self, c_in, d_model):
+        super(TokenEmbedding, self).__init__()
+        padding = 1 if torch.__version__ >= '1.5.0' else 2
+        self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
+                                   kernel_size=3, padding=padding, padding_mode='circular', bias=False)
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_in', nonlinearity='leaky_relu')
+
+    def forward(self, x):
+        x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
+        return x
+
+
+class FixedEmbedding(nn.Module):
+    def __init__(self, c_in, d_model):
+        super(FixedEmbedding, self).__init__()
+
+        w = torch.zeros(c_in, d_model).float()
+        w.require_grad = False
+
+        position = torch.arange(0, c_in).float().unsqueeze(1)
+        div_term = (torch.arange(0, d_model, 2).float()
+                    * -(math.log(10000.0) / d_model)).exp()
+
+        w[:, 0::2] = torch.sin(position * div_term)
+        w[:, 1::2] = torch.cos(position * div_term)
+
+        self.emb = nn.Embedding(c_in, d_model)
+        self.emb.weight = nn.Parameter(w, requires_grad=False)
+
+    def forward(self, x):
+        return self.emb(x).detach()
+
+
+class TemporalEmbedding(nn.Module):
+    def __init__(self, d_model, embed_type='fixed', freq='h'):
+        super(TemporalEmbedding, self).__init__()
+
+        minute_size = 4
+        hour_size = 24
+        weekday_size = 7
+        day_size = 32
+        month_size = 13
+
+        Embed = FixedEmbedding if embed_type == 'fixed' else nn.Embedding
+        if freq == 't':
+            self.minute_embed = Embed(minute_size, d_model)
+        self.hour_embed = Embed(hour_size, d_model)
+        self.weekday_embed = Embed(weekday_size, d_model)
+        self.day_embed = Embed(day_size, d_model)
+        self.month_embed = Embed(month_size, d_model)
+
+    def forward(self, x):
+        x = x.long()
+        minute_x = self.minute_embed(x[:, :, 4]) if hasattr(
+            self, 'minute_embed') else 0.
+        hour_x = self.hour_embed(x[:, :, 3])
+        weekday_x = self.weekday_embed(x[:, :, 2])
+        day_x = self.day_embed(x[:, :, 1])
+        month_x = self.month_embed(x[:, :, 0])
+
+        return hour_x + weekday_x + day_x + month_x + minute_x
+
+
+class TimeFeatureEmbedding(nn.Module):
+    def __init__(self, d_model, embed_type='timeF', freq='h'):
+        super(TimeFeatureEmbedding, self).__init__()
+
+        freq_map = {'h': 4, 't': 5, 's': 6,
+                    'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3}
+        d_inp = freq_map[freq]
+        self.embed = nn.Linear(d_inp, d_model, bias=False)
+
+    def forward(self, x):
+        return self.embed(x)
+
+
+class DataEmbedding(nn.Module):
+    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
+        super(DataEmbedding, self).__init__()
+
+        self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
+        self.position_embedding = PositionalEmbedding(d_model=d_model)
+        self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
+                                                    freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
+            d_model=d_model, embed_type=embed_type, freq=freq)
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, x, x_mark):
+        if x_mark is None:
+            x = self.value_embedding(x) + self.position_embedding(x)
+        else:
+            x = self.value_embedding(
+                x) + self.temporal_embedding(x_mark) + self.position_embedding(x)
+        return self.dropout(x)
+
+class moving_avg(nn.Module):
+    """
+    Moving average block to highlight the trend of time series
+    """
+    def __init__(self, kernel_size, stride):
+        super(moving_avg, self).__init__()
+        self.kernel_size = kernel_size
+        self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)
+
+    def forward(self, x):
+        # padding on the both ends of time series
+        front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)
+        end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)
+        x = torch.cat([front, x, end], dim=1)
+        x = self.avg(x.permute(0, 2, 1))
+        x = x.permute(0, 2, 1)
+        return x
+
+
+class series_decomp(nn.Module):
+    """
+    Series decomposition block
+    """
+    def __init__(self, kernel_size):
+        super(series_decomp, self).__init__()
+        self.moving_avg = moving_avg(kernel_size, stride=1)
+
+    def forward(self, x):
+        moving_mean = self.moving_avg(x)
+        res = x - moving_mean
+        return res, moving_mean
+class DataEmbedding_wo_pos(nn.Module):
+    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
+        super(DataEmbedding_wo_pos, self).__init__()
+
+        self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
+        self.position_embedding = PositionalEmbedding(d_model=d_model)
+        self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
+                                                    freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
+            d_model=d_model, embed_type=embed_type, freq=freq)
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, x, x_mark):
+        if x_mark == None:
+            x = self.value_embedding(x)
+        else:
+            x = self.value_embedding(x) + self.temporal_embedding(x_mark)
+        return self.dropout(x)
+class DataEmbedding_inverted(nn.Module):
+    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
+        super(DataEmbedding_inverted, self).__init__()
+        self.value_embedding = nn.Linear(c_in, d_model)
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, x, x_mark):
+        x = x.permute(0, 2, 1)
+
+        # x: [Batch Variate Time]
+        if x_mark is None:
+
+            x = self.value_embedding(x)
+        else:
+            x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1))
+        return self.dropout(x)
+

layers/Mamba_EncDec.py

@@ -0,0 +1,118 @@
+import concurrent.futures
+import threading
+
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+from mamba_ssm import Mamba
+from layers.SelfAttention_Family import FullAttention, AttentionLayer
+
+import torch
+import torch.nn as nn
+
+class FeedForwardNetwork(nn.Module):
+    def __init__(self, nvars, dmodel, dff, drop=0.1):
+        super(FeedForwardNetwork, self).__init__()
+        
+
+        self.ffn1pw1 = nn.Conv1d(in_channels=nvars * dmodel, out_channels=nvars * dff, 
+                                 kernel_size=1, stride=1, padding=0, dilation=1, groups=nvars)
+        
+
+        self.ffn1act = nn.GELU()
+        
+
+        self.ffn1pw2 = nn.Conv1d(in_channels=nvars * dff, out_channels=nvars * dmodel, 
+                                 kernel_size=1, stride=1, padding=0, dilation=1, groups=nvars)
+
+        self.ffn1drop1 = nn.Dropout(drop)
+        self.ffn1drop2 = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.ffn1drop1(self.ffn1pw1(x))  
+        x = self.ffn1act(x)                  
+        x = self.ffn1drop2(self.ffn1pw2(x)) 
+    
+        return x
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, attention, attention_r, d_model, d_ff=None, dropout=0.1, activation="relu"):
+        super(EncoderLayer, self).__init__()
+        d_ff = d_ff or 4 * d_model
+        self.attention = attention
+        self.attention_r = attention_r
+
+        self.ffn1 = FeedForwardNetwork(nvars=1, dmodel=d_model, dff=d_ff)
+        self.ffn2 = FeedForwardNetwork(nvars=1, dmodel=d_ff, dff=d_model)                                    
+        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
+        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+        self.man = Mamba(
+            d_model=11,  # Model dimension d_model
+            d_state=16,  # SSM state expansion factor
+            d_conv=2,  # Local convolution width
+            expand=1,  # Block expansion factor)
+        )
+        self.man2 = Mamba(
+            d_model=11,  # Model dimension d_model
+            d_state=16,  # SSM state expansion factor
+            d_conv=2,  # Local convolution width
+            expand=1,  # Block expansion factor)
+        )
+        self.a = AttentionLayer(
+                        FullAttention(False, 2, attention_dropout=0.1,
+                                      output_attention=True), 11,1)
+    def forward(self, x, attn_mask=None, tau=None, delta=None):
+
+        x = x.permute(0, 2, 1)
+        x = self.ffn1(x)
+        x = self.ffn2(x) 
+        x = x.permute(0, 2, 1)
+        new_x = self.attention(x) + self.attention_r(x.flip(dims=[1])).flip(dims=[1])
+        attn = 1
+
+        x = x + new_x #torch.Size([32, 11, 256])
+        # print(x.shape)
+        y = x = self.norm1(x)
+
+        # print(y.shape)
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
+        y = self.dropout(self.conv2(y).transpose(-1, 1))
+        
+        return self.norm2(x + y), attn
+
+
+class Encoder(nn.Module):
+    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
+        super(Encoder, self).__init__()
+        self.attn_layers = nn.ModuleList(attn_layers)
+        self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None
+        self.norm = norm_layer
+
+    def forward(self, x, attn_mask=None, tau=None, delta=None):
+        # x [B, L, D]
+        # print(x.shape)
+        attns = []
+        if self.conv_layers is not None:
+            for i, (attn_layer, conv_layer) in enumerate(zip(self.attn_layers, self.conv_layers)):
+                delta = delta if i == 0 else None
+                x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
+                x = conv_layer(x)
+                attns.append(attn)
+            x, attn = self.attn_layers[-1](x, tau=tau, delta=None)
+            attns.append(attn)
+        else:
+            for attn_layer in self.attn_layers:
+                x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
+                
+                attns.append(attn)
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x, attns
+

layers/SelfAttention_Family.py

@@ -0,0 +1,74 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from math import sqrt
+from einops import rearrange
+
+
+class FullAttention(nn.Module):
+    def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
+        super(FullAttention, self).__init__()
+        self.scale = scale
+        self.mask_flag = mask_flag
+        self.output_attention = output_attention
+        self.dropout = nn.Dropout(attention_dropout)
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        B, L, H, E = queries.shape
+        _, S, _, D = values.shape
+        scale = self.scale or 1. / sqrt(E)
+
+        scores = torch.einsum("blhe,bshe->bhls", queries, keys)
+
+        if self.mask_flag:
+            if attn_mask is None:
+                attn_mask = TriangularCausalMask(B, L, device=queries.device)
+
+            scores.masked_fill_(attn_mask.mask, -np.inf)
+
+        A = self.dropout(torch.softmax(scale * scores, dim=-1))
+        V = torch.einsum("bhls,bshd->blhd", A, values)
+
+        if self.output_attention:
+            return (V.contiguous(), A)
+        else:
+            return (V.contiguous(), None)
+
+
+class AttentionLayer(nn.Module):
+    def __init__(self, attention, d_model, n_heads, d_keys=None,
+                 d_values=None):
+        super(AttentionLayer, self).__init__()
+
+        d_keys = d_keys or (d_model // n_heads)
+        d_values = d_values or (d_model // n_heads)
+
+        self.inner_attention = attention
+        self.query_projection = nn.Linear(d_model, d_keys * n_heads)
+        self.key_projection = nn.Linear(d_model, d_keys * n_heads)
+        self.value_projection = nn.Linear(d_model, d_values * n_heads)
+        self.out_projection = nn.Linear(d_values * n_heads, d_model)
+        self.n_heads = n_heads
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        B, L, _ = queries.shape
+        _, S, _ = keys.shape
+        H = self.n_heads
+
+        queries = self.query_projection(queries).view(B, L, H, -1)
+        keys = self.key_projection(keys).view(B, S, H, -1)
+        values = self.value_projection(values).view(B, S, H, -1)
+
+        out, attn = self.inner_attention(
+            queries,
+            keys,
+            values,
+            attn_mask,
+            tau=tau,
+            delta=delta
+        )
+        out = out.view(B, L, -1)
+
+        return self.out_projection(out), attn
+
+

layers/__pycache__/1.py

@@ -0,0 +1 @@
+undefined

model/MambaClimateNet.py

@@ -0,0 +1,84 @@
+import torch
+import torch.nn as nn
+import sys
+from layers.Mamba_EncDec import Encoder, EncoderLayer
+from layers.Embed import DataEmbedding_inverted
+
+from mamba_ssm import Mamba
+
+
+
+class Model(nn.Module):
+
+    def __init__(self, configs):
+        super(Model, self).__init__()
+        self.seq_len = configs.seq_len
+        self.pred_len = configs.pred_len
+        self.output_attention = configs.output_attention
+        self.use_norm = configs.use_norm
+        # Embedding
+        self.enc_embedding = DataEmbedding_inverted(configs.seq_len, configs.d_model, configs.embed, configs.freq,
+                                                    configs.dropout)
+        self.class_strategy = configs.class_strategy
+        # Encoder-only architecture
+        self.encoder = Encoder(
+            [
+                EncoderLayer(
+                        Mamba(
+                            d_model=configs.d_model,  # Model dimension d_model
+                            d_state=configs.d_state,  # SSM state expansion factor
+                            d_conv=2,  # Local convolution width
+                            expand=1,  # Block expansion factor)
+                        ),
+                        Mamba(
+                            d_model=configs.d_model,  # Model dimension d_model
+                            d_state=configs.d_state,  # SSM state expansion factor
+                            d_conv=2,  # Local convolution width
+                            expand=1,  # Block expansion factor)
+                        ),
+                    configs.d_model,
+                    configs.d_ff,
+                    dropout=configs.dropout,
+                    activation=configs.activation
+                ) for l in range(configs.e_layers)
+            ],
+            norm_layer=torch.nn.LayerNorm(configs.d_model)
+        )
+        self.projector = nn.Linear(configs.d_model, configs.pred_len, bias=True)
+
+
+    def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
+        if self.use_norm:
+            # Normalization from Non-stationary Transformer
+            means = x_enc.mean(1, keepdim=True).detach()
+            x_enc = x_enc - means
+            stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
+            x_enc /= stdev
+
+        _, _, N = x_enc.shape # B L N
+        # B: batch_size;    E: d_model; 
+        # L: seq_len;       S: pred_len;
+        # N: number of variate (tokens), can also includes covariates
+
+        # Embedding
+        # B L N -> B N E                (B L N -> B L E in the vanilla Transformer)
+        enc_out = self.enc_embedding(x_enc, x_mark_enc) # covariates (e.g timestamp) can be also embedded as tokens
+
+        # B N E -> B N E                (B L E -> B L E in the vanilla Transformer)
+        # the dimensions of embedded time series has been inverted, and then processed by native attn, layernorm and ffn modules
+        enc_out, attns = self.encoder(enc_out, attn_mask=None)
+        # B N E -> B N S -> B S N 
+        dec_out = self.projector(enc_out).permute(0, 2, 1)[:, :, :N] # filter the covariates
+
+        if self.use_norm:
+            # De-Normalization from Non-stationary Transformer
+            dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
+            dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
+
+        return dec_out
+
+
+    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
+        dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
+        return dec_out[:, -self.pred_len:, :]  # [B, L, D]
+

model/__pycache__/1.py

@@ -0,0 +1 @@
+undefined