pred_stock_future.py

def pred_stock_future(X_test,
                                           future_length,
                                           sample_nbr=10):
    
    #sorry for that, window_size is a global variable, and so are X_train and Xs
    global window_size
    global X_train
    global Xs
    global scaler
    
    #creating auxiliar variables for future prediction
    preds_test = []
    test_begin = X_test[0:1, :, :]
    test_deque = deque(test_begin[0,:,0].tolist(), maxlen=window_size)

    idx_pred = np.arange(len(X_train), len(Xs))
    
    #predict it and append to list
    for i in range(len(X_test)):
        #print(i)
        as_net_input = torch.tensor(test_deque).unsqueeze(0).unsqueeze(2)
        pred = [net(as_net_input).cpu().item() for i in range(sample_nbr)]
        
        
        test_deque.append(torch.tensor(pred).mean().cpu().item())
        preds_test.append(pred)
        
        if i % future_length == 0:
            #our inptus become the i index of our X_test
            #That tweak just helps us with shape issues
            test_begin = X_test[i:i+1, :, :]
            test_deque = deque(test_begin[0,:,0].tolist(), maxlen=window_size)

    #preds_test = np.array(preds_test).reshape(-1, 1)
    #preds_test_unscaled = scaler.inverse_transform(preds_test)
    
    return idx_pred, preds_test