LSTM期货预测资料输入\输出问题

1. 我将进行一次LSTM期货价格预测，原本的代码是仅读入收盘价的特征，但我尝试用网上的代码修改后，试图将其他的特征也读入作为模型训练，以此来预测价格，并输出。但我不知道哪个地方错误，还只是个小白还请教学与协助。
2. 代码如下 import numpy as np import matplotlib.pyplot as plt import pandas as pd dataset_train = pd.read_csv('A.csv') training_set = dataset_train.iloc[:, 1:8].values dataset_train.head() from sklearn.preprocessing import MinMaxScaler sc = MinMaxScaler(feature_range = (0, 1)) training_set_scaled = sc.fit_transform(training_set) X_train = [] y_train = [] for i in range(60, 1311): X_train.append(training_set_scaled[i-60:i, 0]) y_train.append(training_set_scaled[i, 0]) X_train, y_train = np.array(X_train), np.array(y_train) X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))

import tensorflow as tf
model = tf.keras.Sequential()
model.add(tf.keras.layers.LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.LSTM(units = 50, return_sequences = True))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.LSTM(units = 50, return_sequences = True))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.LSTM(units = 50))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(units = 1))
model.compile(optimizer = 'adam', loss = 'mean_squared_error')
model.fit(X_train, y_train, epochs = 1, batch_size = 128 )

dataset_test = pd.read_csv('B.csv')
real_stock_price = dataset_test.iloc[:, 1:8].values

dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1,1)
inputs = sc.transform(inputs)
X_test = []
for i in range(60, 126,5):
X_test.append(inputs[i-60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = model.predict(X_test)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)

plt.plot(real_stock_price, color = 'black', label = 'TXF1 Price')
plt.plot(predicted_stock_price, color = 'green', label = 'Predicted TXF1 Price')
plt.title('TXF1 Prediction')
plt.xlabel('Time')
plt.ylabel('TXF1 Price')
plt.legend()
plt.show()

model.add(tf.keras.layers.LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
你的input_shape还是1，你在X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))这里输出下 x_test.shape看下