python - 如何在 keras 顺序模型中获取来自未来的输出

Question

我是 python 的初学者，我对与金融相关的机器学习很感兴趣。我创建了一个模型来预测从 csv 文件读取的未来价格，我创建了神经网络，我得到它的损失非常小，但无法提取输出，或者我只是没有创建必要的层。我将不胜感激这方面的帮助。提前致谢。

model = Sequential()
model.add(LSTM(256, input_shape=(1,1)))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])

model.fit(train_x, train_y, epochs=30, batch_size=1, verbose=1)
score = model.evaluate(train_x, train_y, verbose=0)
print('Keras model loss = ', score[0])
print('Keras model accuracy = ', score[1])
   

train_predictions = model.predict(train_x)
test_predictions = model.predict(test_x)

train_predictions = scaler.inverse_transform(train_predictions)
train_y = scaler.inverse_transform([train_y])

train_predict_plot = np.empty_like(scaled_data)
train_predict_plot[:,:] = np.nan
train_predict_plot[1:len(train_predictions)+1, :] = train_predictions

test_predict_plot = np.empty_like(scaled_data)
test_predict_plot[:,:] = np.nan
test_predict_plot[

plt.plot(scaler.inverse_transform(scaled_data))
plt.plot(train_predict_plot)
plt.plot(test_predict_plot)
plt.show()

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