PyTorch+LSTM实现单变量时间序列预测

 from sklearn.preprocessing import StandardScaler  
   
 # Fit scalers  
 scalers = {}  
 for x in df.columns:  
   scalers[x] = StandardScaler().fit(df[x].values.reshape(-1, 1))  
   
 # Transform data via scalers  
 norm_df = df.copy()  
 for i, key in enumerate(scalers.keys()):  
   norm = scalers[key].transform(norm_df.iloc[:, i].values.reshape(-1, 1))  
   norm_df.iloc[:, i] = norm

 # 如上所示，定义一个创建序列和目标的函数  
 def generate_sequences(df: pd.DataFrame, tw: int, pw: int, target_columns, drop_targets=False):  
   '''  
  df: Pandas DataFrame of the univariate time-series  
  tw: Training Window - Integer defining how many steps to look back  
  pw: Prediction Window - Integer defining how many steps forward to predict  
   
  returns: dictionary of sequences and targets for all sequences  
  '''  
   data = dict() # Store results into a dictionary  
   L = len(df)  
   for i in range(L-tw):  
     # Option to drop target from dataframe  
     if drop_targets:  
       df.drop(target_columns, axis=1, inplace=True)  
   
     # Get current sequence    
     sequence = df[i:i+tw].values  
     # Get values right after the current sequence  
     target = df[i+tw:i+tw+pw][target_columns].values  
     data[i] = {'sequence': sequence, 'target': target}  
   return data

 class SequenceDataset(Dataset):  
   
   def __init__(self, df):  
     self.data = df  
   
   def __getitem__(self, idx):  
     sample = self.data[idx]  
     return torch.Tensor(sample['sequence']), torch.Tensor(sample['target'])  
     
   def __len__(self):  
     return len(self.data)

 # 这里我们为我们的模型定义属性  
   
 BATCH_SIZE = 16 # Training batch size  
 split = 0.8 # Train/Test Split ratio  
   
 sequences = generate_sequences(norm_df.dcoilwtico.to_frame(), sequence_len, nout, 'dcoilwtico')  
 dataset = SequenceDataset(sequences)  
   
 # 根据拆分比例拆分数据，并将每个子集加载到单独的DataLoader对象中  
 train_len = int(len(dataset)*split)  
 lens = [train_len, len(dataset)-train_len]  
 train_ds, test_ds = random_split(dataset, lens)  
 trainloader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)  
 testloader = DataLoader(test_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)

 class LSTMForecaster(nn.Module):  
  
   def __init__(self, n_features, n_hidden, n_outputs, sequence_len, n_lstm_layers=1, n_deep_layers=10, use_cuda=False, dropout=0.2):  
     '''  
    n_features: number of input features (1 for univariate forecasting)  
    n_hidden: number of neurons in each hidden layer  
    n_outputs: number of outputs to predict for each training example  
    n_deep_layers: number of hidden dense layers after the lstm layer  
    sequence_len: number of steps to look back at for prediction  
    dropout: float (0 < dropout < 1) dropout ratio between dense layers  
    '''  
     super().__init__()  
   
     self.n_lstm_layers = n_lstm_layers  
     self.nhid = n_hidden  
     self.use_cuda = use_cuda # set option for device selection  
   
     # LSTM Layer  
     self.lstm = nn.LSTM(n_features,  
                         n_hidden,  
                         num_layers=n_lstm_layers,  
                         batch_first=True) # As we have transformed our data in this way  
       
     # first dense after lstm  
     self.fc1 = nn.Linear(n_hidden * sequence_len, n_hidden)  
     # Dropout layer  
     self.dropout = nn.Dropout(p=dropout)  
   
     # Create fully connected layers (n_hidden x n_deep_layers)  
     dnn_layers = []  
     for i in range(n_deep_layers):  
       # Last layer (n_hidden x n_outputs)  
       if i == n_deep_layers - 1:  
         dnn_layers.append(nn.ReLU())  
         dnn_layers.append(nn.Linear(nhid, n_outputs))  
       # All other layers (n_hidden x n_hidden) with dropout option  
       else:  
         dnn_layers.append(nn.ReLU())  
         dnn_layers.append(nn.Linear(nhid, nhid))  
         if dropout:  
           dnn_layers.append(nn.Dropout(p=dropout))  
     # compile DNN layers  
     self.dnn = nn.Sequential(*dnn_layers)  
   
   def forward(self, x):  
   
     # Initialize hidden state  
     hidden_state = torch.zeros(self.n_lstm_layers, x.shape[0], self.nhid)  
     cell_state = torch.zeros(self.n_lstm_layers, x.shape[0], self.nhid)  
   
     # move hidden state to device  
     if self.use_cuda:  
       hidden_state = hidden_state.to(device)  
       cell_state = cell_state.to(device)  
           
     self.hidden = (hidden_state, cell_state)  
   
     # Forward Pass  
     x, h = self.lstm(x, self.hidden) # LSTM  
     x = self.dropout(x.contiguous().view(x.shape[0], -1)) # Flatten lstm out  
     x = self.fc1(x) # First Dense  
     return self.dnn(x) # Pass forward through fully connected DNN.

 nhid = 50 # Number of nodes in the hidden layer  
 n_dnn_layers = 5 # Number of hidden fully connected layers  
 nout = 1 # Prediction Window  
 sequence_len = 180 # Training Window  
   
 # Number of features (since this is a univariate timeseries we'll set  
 # this to 1 -- multivariate analysis is coming in the future)  
 ninp = 1  
   
 # Device selection (CPU | GPU)  
 USE_CUDA = torch.cuda.is_available()  
 device = 'cuda' if USE_CUDA else 'cpu'  
   
 # Initialize the model  
 model = LSTMForecaster(ninp, nhid, nout, sequence_len, n_deep_layers=n_dnn_layers, use_cuda=USE_CUDA).to(device)

 # Set learning rate and number of epochs to train over  
 lr = 4e-4  
 n_epochs = 20  
   
 # Initialize the loss function and optimizer  
 criterion = nn.MSELoss().to(device)  
 optimizer = torch.optim.AdamW(model.parameters(), lr=lr)

# Lists to store training and validation losses  
t_losses, v_losses = [], []  
# Loop over epochs  
for epoch in range(n_epochs):  
  train_loss, valid_loss = 0.0, 0.0  
  
  # train step  
  model.train()  
  # Loop over train dataset  
  for x, y in trainloader:  
    optimizer.zero_grad()  
    # move inputs to device  
    x = x.to(device)  
    y  = y.squeeze().to(device)  
    # Forward Pass  
    preds = model(x).squeeze()  
    loss = criterion(preds, y) # compute batch loss  
    train_loss += loss.item()  
    loss.backward()  
    optimizer.step()  
  epoch_loss = train_loss / len(trainloader)  
  t_losses.append(epoch_loss)  
    
  # validation step  
  model.eval()  
  # Loop over validation dataset  
  for x, y in testloader:  
    with torch.no_grad():  
      x, y = x.to(device), y.squeeze().to(device)  
      preds = model(x).squeeze()  
      error = criterion(preds, y)  
    valid_loss += error.item()  
  valid_loss = valid_loss / len(testloader)  
  v_losses.append(valid_loss)  
        
  print(f'{epoch} - train: {epoch_loss}, valid: {valid_loss}')  
plot_losses(t_losses, v_losses)  

def make_predictions_from_dataloader(model, unshuffled_dataloader):  
  model.eval()  
  predictions, actuals = [], []  
  for x, y in unshuffled_dataloader:  
    with torch.no_grad():  
      p = model(x)  
      predictions.append(p)  
      actuals.append(y.squeeze())  
  predictions = torch.cat(predictions).numpy()  
  actuals = torch.cat(actuals).numpy()  
  return predictions.squeeze(), actuals  

def one_step_forecast(model, history):  
      '''  
      model: PyTorch model object  
      history: a sequence of values representing the latest values of the time   
      series, requirement -> len(history.shape) == 2  
      
      outputs a single value which is the prediction of the next value in the  
      sequence.  
      '''  
      model.cpu()  
      model.eval()  
      with torch.no_grad():  
        pre = torch.Tensor(history).unsqueeze(0)  
        pred = self.model(pre)  
      return pred.detach().numpy().reshape(-1)  
  
  def n_step_forecast(data: pd.DataFrame, target: str, tw: int, n: int, forecast_from: int=None, plot=False):  
      '''  
      n: integer defining how many steps to forecast  
      forecast_from: integer defining which index to forecast from. None if  
      you want to forecast from the end.  
      plot: True if you want to output a plot of the forecast, False if not.  
      '''  
      history = data[target].copy().to_frame()  
        
      # Create initial sequence input based on where in the series to forecast   
      # from.  
      if forecast_from:  
        pre = list(history[forecast_from - tw : forecast_from][target].values)  
      else:  
        pre = list(history[self.target])[-tw:]  
  
      # Call one_step_forecast n times and append prediction to history  
      for i, step in enumerate(range(n)):  
        pre_ = np.array(pre[-tw:]).reshape(-1, 1)  
        forecast = self.one_step_forecast(pre_).squeeze()  
        pre.append(forecast)  
        
      # The rest of this is just to add the forecast to the correct time of   
      # the history series  
      res = history.copy()  
      ls = [np.nan for i in range(len(history))]  
  
      # Note: I have not handled the edge case where the start index + n is   
      # before the end of the dataset and crosses past it.  
      if forecast_from:  
        ls[forecast_from : forecast_from + n] = list(np.array(pre[-n:]))  
        res['forecast'] = ls  
        res.columns = ['actual', 'forecast']  
      else:  
        fc = ls + list(np.array(pre[-n:]))  
        ls = ls + [np.nan for i in range(len(pre[-n:]))]  
        ls[:len(history)] = history[self.target].values  
        res = pd.DataFrame([ls, fc], index=['actual', 'forecast']).T  
      return res