前言
- 今年时间序列SOTA，
DLinear模型，论文下载链接，也可以看我写的论文解析当然最好是读原文。 Dlinear，NLinear模型Github项目地址，下载项目文件- 这里提供我写过注释的项目文件，下载地址
参数设定模块(run_longExp)
- 首先打开
run_longExp.py文件保证在不修改任何参数的情况下，代码可以跑通，这里windows系统需要将代码中--is_training、--model_id、--model、--data参数中required=True选项删除，否则会报错。--num_workers参数需要置为0。 - 其次需要在项目文件夹下新建子文件夹
data用来存放训练数据，可以使用ETTh1数据，这里提供下载地址 - 运行
run_longExp.py训练完成不报错就成功了
参数含义
- 下面是各参数含义（注释）
parser = argparse.ArgumentParser(description='Autoformer & Transformer family for Time Series Forecasting')
# 是否进行训练
parser.add_argument('--is_training', type=int, default=1, help='status')
# 模型前缀
parser.add_argument('--model_id', type=str, default='test', help='model id')
# 选择模型(可选模型有Autoformer, Informer, Transformer，DLinear，NLinear)
parser.add_argument('--model', type=str, default='DLinear',
help='model name, options: [Autoformer, Informer, Transformer]')
# 数据选择
parser.add_argument('--data', type=str, default='ETTh1', help='dataset type')
# 数据存放路径
parser.add_argument('--root_path', type=str, default='./data/', help='root path of the data file')
# 数据完整名称
parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')
# 预测类型(多变量预测、单变量预测、多元预测单变量)
parser.add_argument('--features', type=str, default='M',
help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate')
# 如果选择单变量预测或多元预测单变量，需要指定预测的列
parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
# 数据重采样格式
parser.add_argument('--freq', type=str, default='h',
help='freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h')
# 模型存放文件夹
parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
# 时间窗口长度
parser.add_argument('--seq_len', type=int, default=96, help='input sequence length')
# 先验序列长度
parser.add_argument('--label_len', type=int, default=48, help='start token length')
# 要预测的序列长度
parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length')
# 针对DLinear是否为每个变量（通道）单独建立一个线性层
parser.add_argument('--individual', action='store_true', default=False, help='DLinear: a linear layer for each variate(channel) individually')
# 嵌入策略选择
parser.add_argument('--embed_type', type=int, default=0, help='0: default 1: value embedding + temporal embedding + positional embedding 2: value embedding + temporal embedding 3: value embedding + positional embedding 4: value embedding')
# 编码器default参数为特征列数
parser.add_argument('--enc_in', type=int, default=7, help='encoder input size') # DLinear with --individual, use this hyperparameter as the number of channels
# 解码器default参数与编码器相同
parser.add_argument('--dec_in', type=int, default=7, help='decoder input size')
parser.add_argument('--c_out', type=int, default=7, help='output size')
# 模型宽度
parser.add_argument('--d_model', type=int, default=512, help='dimension of model')
# 多头注意力机制头数
parser.add_argument('--n_heads', type=int, default=8, help='num of heads')
# 模型中encoder层数
parser.add_argument('--e_layers', type=int, default=2, help='num of encoder layers')
# 模型中decoder层数
parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers')
# 全连接层神经元个数
parser.add_argument('--d_ff', type=int, default=2048, help='dimension of fcn')
# 窗口平均线的窗口大小
parser.add_argument('--moving_avg', type=int, default=25, help='window size of moving average')
# 采样因子数
parser.add_argument('--factor', type=int, default=1, help='attn factor')
# 是否需要序列长度衰减
parser.add_argument('--distil', action='store_false',
help='whether to use distilling in encoder, using this argument means not using distilling',
default=True)
# drop_out率
parser.add_argument('--dropout', type=float, default=0.05, help='dropout')
# 时间特征编码方式
parser.add_argument('--embed', type=str, default='timeF',
help='time features encoding, options:[timeF, fixed, learned]')
# 激活函数
parser.add_argument('--activation', type=str, default='gelu', help='activation')
# 是否输出attention
parser.add_argument('--output_attention', action='store_true', help='whether to output attention in ecoder')
# 是否进行预测
parser.add_argument('--do_predict', action='store_false', help='whether to predict unseen future data')
# 多线程
parser.add_argument('--num_workers', type=int, default=0, help='data loader num workers')
# 训练轮数
parser.add_argument('--itr', type=int, default=1, help='experiments times')
# 训练迭代次数
parser.add_argument('--train_epochs', type=int, default=100, help='train epochs')
# batch_size大小
parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data')
# early stopping检测间隔
parser.add_argument('--patience', type=int, default=3, help='early stopping patience')
# 学习率
parser.add_argument('--learning_rate', type=float, default=0.0001, help='optimizer learning rate')
parser.add_argument('--des', type=str, default='test', help='exp description')
# loss函数
parser.add_argument('--loss', type=str, default='mse', help='loss function')
# 学习率衰减参数
parser.add_argument('--lradj', type=str, default='type1', help='adjust learning rate')
# 是否使用自动混合精度训练
parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision training', default=False)
# 是否使用GPU训练
parser.add_argument('--use_gpu', type=bool, default=True, help='use gpu')
parser.add_argument('--gpu', type=int, default=0, help='gpu')
# GPU分布式训练
parser.add_argument('--use_multi_gpu', action='store_true', help='use multiple gpus', default=False)
# 多GPU训练
parser.add_argument('--devices', type=str, default='0,1,2,3', help='device ids of multile gpus')
parser.add_argument('--test_flop', action='store_true', default=False, help='See utils/tools for usage')
# 取参数表
args = parser.parse_args()
# 获取GPU
args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False
我们在exp.train(setting)行打上断点跳到训练主函数exp_main.py。
数据处理模块
- 在
_get_data中找到数据处理函数data_provider.py点击进入，可以看到各标准数据集处理方法：
data_dict = {
'ETTh1': Dataset_ETT_hour,
'ETTh2': Dataset_ETT_hour,
'ETTm1': Dataset_ETT_minute,
'ETTm2': Dataset_ETT_minute,
'Custom': Dataset_Custom,
}
- 由于我们的数据集是
ETTh1，那么数据处理的方式为Dataset_ETT_hour，我们进入data_loader.py文件，找到Dataset_ETT_hour类 __init__主要用于传各类参数，这里不过多赘述，主要对__read_data__进行说明
def __read_data__(self):
# 数据标准化实例
self.scaler = StandardScaler()
# 读取数据
df_raw = pd.read_csv(os.path.join(self.root_path,
self.data_path))
# 计算数据起始点
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]
# 如果预测对象为多变量预测或多元预测单变量
if self.features == 'M' or self.features == 'MS':
# 取除日期列的其他所有列
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
# 若预测类型为S(单特征预测单特征)
elif self.features == 'S':
# 取特征列
df_data = df_raw[[self.target]]
# 将数据进行归一化
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
# 取日期列
df_stamp = df_raw[['date']][border1:border2]
# 利用pandas将数据转换为日期格式
df_stamp['date'] = pd.to_datetime(df_stamp.date)
# 构建时间特征
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:
# 时间特征构造函数
data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)
# 转置
data_stamp = data_stamp.transpose(1, 0)
# 取数据特征列
self.data_x = data[border1:border2]
self.data_y = data[border1:border2]
self.data_stamp = data_stamp
- 需要注意的是
time_features函数，用来提取日期特征，比如't':['month','day','weekday','hour','minute']，表示提取月，天，周，小时，分钟。可以打开timefeatures.py文件进行查阅，同样后期也可以加一些日期编码进去。 - 同样的，对
__getitem__进行说明
def __getitem__(self, index):
# 随机取得标签
s_begin = index
# 训练区间
s_end = s_begin + self.seq_len
# 有标签区间+无标签区间(预测时间步长)
r_begin = s_end - self.label_len
r_end = r_begin + self.label_len + self.pred_len
# 取训练数据
seq_x = self.data_x[s_begin:s_end]
seq_y = self.data_y[r_begin:r_end]
# 取训练数据对应时间特征
seq_x_mark = self.data_stamp[s_begin:s_end]
# 取有标签区间+无标签区间(预测时间步长)对应时间特征
seq_y_mark = self.data_stamp[r_begin:r_end]
return seq_x, seq_y, seq_x_mark, seq_y_mark
网络架构
- 我们回到
exp_main.py文件中的train函数。
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)
# 记录时间
time_now = time.time()
# 训练steps
train_steps = len(train_loader)
# 早停策略
early_stopping = EarlyStopping(patience=self.args.patience, verbose=True)
# 优化器
model_optim = self._select_optimizer()
# 损失函数(MSE)
criterion = self._select_criterion()
# 分布式训练(windows一般不推荐)
if self.args.use_amp:
scaler = torch.cuda.amp.GradScaler()
# 训练次数
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
# 梯度归零
model_optim.zero_grad()
# 取训练数据
batch_x = batch_x.float().to(self.device)
batch_y = batch_y.float().to(self.device)
batch_x_mark = batch_x_mark.float().to(self.device)
batch_y_mark = batch_y_mark.float().to(self.device)
# decoder输入
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 'Linear' in self.args.model:
outputs = self.model(batch_x)
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())
else:
# 如果模型是Linear系列
if 'Linear' in self.args.model:
# 得到输出
outputs = self.model(batch_x)
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, batch_y)
# print(outputs.shape,batch_y.shape)
# 如果预测方式为MS，取最后1列否则取第1列
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)
# 计算损失
loss = criterion(outputs, batch_y)
# 将损失放入train_loss列表中
train_loss.append(loss.item())
# 记录训练过程
if (i + 1) % 500 == 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:
# 反向传播
loss.backward()
# 更新梯度
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
# 更新学习率
adjust_learning_rate(model_optim, epoch + 1, self.args)
# 保存模型
best_model_path = path + '/' + 'checkpoint.pth'
self.model.load_state_dict(torch.load(best_model_path))
- 注意模型训练
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)，model中包含DLinear的核心架构（也是最重要的部分），打开项目文件夹下models文件夹，找到DLinear.py文件，打开后找到Model类。直接看forward
def forward(self, x):
# x: [Batch, Input length, Channel]
# 季节与时间趋势性分解
seasonal_init, trend_init = self.decompsition(x)
# 将维度索引2与维度索引1交换
seasonal_init, trend_init = seasonal_init.permute(0,2,1), trend_init.permute(0,2,1)
if self.individual:
seasonal_output = torch.zeros([seasonal_init.size(0),seasonal_init.size(1),self.pred_len],dtype=seasonal_init.dtype).to(seasonal_init.device)
trend_output = torch.zeros([trend_init.size(0),trend_init.size(1),self.pred_len],dtype=trend_init.dtype).to(trend_init.device)
for i in range(self.channels):
# 使用全连接层得到季节性
seasonal_output[:,i,:] = self.Linear_Seasonal[i](seasonal_init[:,i,:])
# 使用全连接层得到趋势性
trend_output[:,i,:] = self.Linear_Trend[i](trend_init[:,i,:])
# 两者共享所有权重
else:
seasonal_output = self.Linear_Seasonal(seasonal_init)
trend_output = self.Linear_Trend(trend_init)
# 将季节性与趋势性相加
x = seasonal_output + trend_output
# 交换维度位置
return x.permute(0,2,1) # to [Batch, Output length, Channel]
- 季节趋势性分解，跳转到
series_decomp类
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):
# 滑动平均
moving_mean = self.moving_avg(x)
# 季节趋势性
res = x - moving_mean
return res, moving_mean
- 季节性和趋势性使用同一全连接神经网络，共享所有权重。使用
nn.Linear函数构建了全连接神经网络
结果展示
- 训练DLinear模型是非常快的，因为丢弃了很多transformer中复杂的计算块，跑一遍ETTh1数据只需要大约1分钟，我用的笔记本上的CPU。
Args in experiment:
Namespace(is_training=1, model_id='test', model='DLinear', data='ETTh1', root_path='./data/', data_path='ETTh1.csv', features='M', target='OT', freq='h', checkpoints='./checkpoints/', seq_len=96, label_len=48, pred_len=96, individual=False, embed_type=0, enc_in=7, dec_in=7, c_out=7, d_model=512, n_heads=8, e_layers=2, d_layers=1, d_ff=2048, moving_avg=25, factor=1, distil=True, dropout=0.05, embed='timeF', activation='gelu', output_attention=False, do_predict=True, num_workers=0, itr=1, train_epochs=100, batch_size=32, patience=3, learning_rate=0.0001, des='test', loss='mse', lradj='type1', use_amp=False, use_gpu=False, gpu=0, use_multi_gpu=False, devices='0,1,2,3', test_flop=False)
Use CPU
>>>>>>>start training : DLinear_rate 0.0001>>>>>>>>>>>>>>>>>>>>>>>>>>
train 8449
val 2785
test 2785
Epoch: 1 cost time: 1.5147898197174072
Epoch: 1, Steps: 264 | Train Loss: 0.6620889 Vali Loss: 0.8593202 Test Loss: 0.5310578
Validation loss decreased (inf --> 0.859320). Saving model ...
Updating learning rate to 0.0001
Epoch: 2 cost time: 1.49473237991333
Epoch: 2, Steps: 264 | Train Loss: 0.4363616 Vali Loss: 0.7708484 Test Loss: 0.4540242
Validation loss decreased (0.859320 --> 0.770848). Saving model ...
Updating learning rate to 5e-05
Epoch: 3 cost time: 1.2200875282287598
Epoch: 3, Steps: 264 | Train Loss: 0.4081523 Vali Loss: 0.7452631 Test Loss: 0.4380584
Validation loss decreased (0.770848 --> 0.745263). Saving model ...
Updating learning rate to 2.5e-05
Epoch: 4 cost time: 1.2776997089385986
Epoch: 4, Steps: 264 | Train Loss: 0.3990288 Vali Loss: 0.7355505 Test Loss: 0.4318272
Validation loss decreased (0.745263 --> 0.735550). Saving model ...
Updating learning rate to 1.25e-05
Epoch: 5 cost time: 1.2430932521820068
Epoch: 5, Steps: 264 | Train Loss: 0.3950030 Vali Loss: 0.7301292 Test Loss: 0.4291500
Validation loss decreased (0.735550 --> 0.730129). Saving model ...
Updating learning rate to 6.25e-06
Epoch: 6 cost time: 1.260094165802002
Epoch: 6, Steps: 264 | Train Loss: 0.3931120 Vali Loss: 0.7285364 Test Loss: 0.4276760
Validation loss decreased (0.730129 --> 0.728536). Saving model ...
Updating learning rate to 3.125e-06
Epoch: 7 cost time: 1.2400920391082764
Epoch: 7, Steps: 264 | Train Loss: 0.3921362 Vali Loss: 0.7272122 Test Loss: 0.4269841
Validation loss decreased (0.728536 --> 0.727212). Saving model ...
Updating learning rate to 1.5625e-06
Epoch: 8 cost time: 1.2691984176635742
Epoch: 8, Steps: 264 | Train Loss: 0.3916254 Vali Loss: 0.7265375 Test Loss: 0.4266387
Validation loss decreased (0.727212 --> 0.726538). Saving model ...
Updating learning rate to 7.8125e-07
Epoch: 9 cost time: 1.31856369972229
Epoch: 9, Steps: 264 | Train Loss: 0.3913689 Vali Loss: 0.7263398 Test Loss: 0.4264523
Validation loss decreased (0.726538 --> 0.726340). Saving model ...
Updating learning rate to 3.90625e-07
Epoch: 10 cost time: 1.3412230014801025
Epoch: 10, Steps: 264 | Train Loss: 0.3912611 Vali Loss: 0.7261187 Test Loss: 0.4263628
Validation loss decreased (0.726340 --> 0.726119). Saving model ...
Updating learning rate to 1.953125e-07
Epoch: 11 cost time: 1.246096134185791
Epoch: 11, Steps: 264 | Train Loss: 0.3911887 Vali Loss: 0.7262567 Test Loss: 0.4263154
EarlyStopping counter: 1 out of 3
Updating learning rate to 9.765625e-08
Epoch: 12 cost time: 1.2540950775146484
Epoch: 12, Steps: 264 | Train Loss: 0.3911324 Vali Loss: 0.7254719 Test Loss: 0.4262920
Validation loss decreased (0.726119 --> 0.725472). Saving model ...
Updating learning rate to 4.8828125e-08
Epoch: 13 cost time: 1.284095287322998
Epoch: 13, Steps: 264 | Train Loss: 0.3911295 Vali Loss: 0.7261668 Test Loss: 0.4262800
EarlyStopping counter: 1 out of 3
Updating learning rate to 2.44140625e-08
Epoch: 14 cost time: 1.3260986804962158
Epoch: 14, Steps: 264 | Train Loss: 0.3911082 Vali Loss: 0.7258070 Test Loss: 0.4262740
EarlyStopping counter: 2 out of 3
Updating learning rate to 1.220703125e-08
Epoch: 15 cost time: 1.2486040592193604
Epoch: 15, Steps: 264 | Train Loss: 0.3911197 Vali Loss: 0.7261318 Test Loss: 0.4262710
EarlyStopping counter: 3 out of 3
Early stopping
>>>>>>>testing : DLinear_rate 0.0001<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
test 2785
mse:0.42629215121269226, mae:0.4337235391139984
>>>>>>>predicting : DLinear_rate 0.0001<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
pred 1
- 模型运行完以后会在
test_results文件夹下生成，模型在测试集上表现情况：
- 如果想要生成季节性、趋势性图，可以打开项目文件夹下的
weight_plot.py文件，将save_root = 'weights_plot/%s'%root.split('/')[1]改成save_root = './weights_plot/'，然后运行。那么在weights_plot文件夹下就能看见季节性与趋势性热力图。
后面如果有时间我会继续写如何使用DLinear定义自己的项目。
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