是的，您可以按照下面的示例进行操作：

import numpy as np
import pandas as pd
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Bidirectional, Dropout
from tensorflow.keras.optimizers import Adam
from sklearn.preprocessing import StandardScaler

data = np.random.randn(100, 5)
df = pd.DataFrame(data, columns=['A', 'B', 'C', 'D', 'E'])

train_size = int(len(df) * 0.8)
train = df[:train_size]
test = df[train_size:]

scaler = StandardScaler()
train_scaled = scaler.fit_transform(train)
test_scaled = scaler.transform(test)

def create_sequences(X, y, time_steps=10):
    Xs, ys = [], []
    for i in range(len(X) - time_steps):
        Xs.append(X[i:i + time_steps])
        ys.append(y[i + time_steps])
    return np.array(Xs), np.array(ys)

X_train, y_train = create_sequences(train_scaled[:, :-1], train_scaled[:, -1])
X_test, y_test = create_sequences(test_scaled[:, :-1], test_scaled[:, -1])

X_train = X_train.reshape(-1, 10, 5)
X_test = X_test.reshape(-1, 10, 5)

# create model
model = Sequential()
model.add(Bidirectional(LSTM(64, input_shape=(10, 5))))
model.add(Dropout(0.5))
model.add(Dense(1))

model.compile(loss='mse', optimizer=Adam(learning_rate=0.001))

model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.1, shuffle=False)

y_pred = model.predict(X_test)

y_test_zeros = np.zeros((len(y_test), 4))
y_test_concat = np.hstack((y_test.reshape(-1,1), y_test_zeros))

y_pred_concat = np.hstack((y_pred, np.zeros((len(y_pred), 4))))
y_pred_scaled = scaler.inverse_transform(y_pred_concat)[:, 0].astype(int)

print("The predicted numbers are:")
for i in range(len(y_pred_scaled)):
    print(y_pred_scaled[i])

该函数返回：

The predicted numbers are:
0
0
0
0
1
-1
0
0

因此，您只需执行以下操作：

y_pred_concat = np.hstack((y_pred, np.zeros((len(y_pred), 4))))
y_pred_scaled = scaler.inverse_transform(y_pred_concat)[:, 0].astype(int)

print("The predicted numbers are:")
for i in range(len(y_pred_scaled)):
    print(y_pred_scaled[i])