Walk Forward Validation in Pytorch LSTM

Question

Im currently building an LSTM Model for predicting stock prices in pytorch. I now want to implement a walk forward validation method, but I couldnt find any resource in how to do that. This is my current training loop:

#%%
lstm1 = LSTM1(num_classes, input_size, hidden_dim, num_layers, X_train_tensors_final.shape[1])
criterion = torch.nn.L1Loss()
optimizer = torch.optim.Adam(lstm1.parameters(), lr=learning_rate) 

for epoch in range(num_epochs):
  outputs = lstm1.forward(X_train_tensors_final) 
  optimizer.zero_grad() #clear gradients
 
     
  loss = criterion(outputs, y_train_tensors)
  
 
  loss.backward() #calculates the loss of the loss function
 
  optimizer.step() #improve from loss, i.e backprop
  if epoch % 100 == 0:
    print("Epoch: %d, loss: %1.5f" % (epoch, loss.item())) 

df_X_ss = ss.transform(df.iloc[:, 0:-1]) 
df_y_mm = ss.transform(df.iloc[:, 0:1]) 

df_X_ss = Variable(torch.Tensor(df_X_ss)) 
df_y_mm = Variable(torch.Tensor(df_y_mm))
df_X_ss = torch.reshape(df_X_ss, (df_X_ss.shape[0], 1, df_X_ss.shape[1])) 

train_predict = lstm1(df_X_ss)
data_predict = train_predict.data.numpy()

The model should now predict one step into the future, then calculate the absolute percentage error. For the next step, the model should use the actual y value instead of the predicted yhat to make its next prediction. What would be the best way of implementing this? Or is there some build in function in pytorch that would do this ?

Walk Forward Validation in Pytorch LSTM

0 Answers