如何使用该模型预测未来值？我尝试将时间步长窗口更改为大于股票数据库的值，但只得到一个错误，说元组索引超出范围。我该如何预测未来值，而不是在现有数据上测试模型？这是我的代码：

import numpy as npimport matplotlib.pyplot as pltimport pandas as pddataset_train = pd.read_csv(r'/path', error_bad_lines = False)training_set = dataset_train.iloc[:, 1:2].valuesfrom sklearn.preprocessing import MinMaxScalersc = MinMaxScaler(feature_range = (0, 1))sc_training_set = sc.fit_transform(training_set)X_train = []y_train = []for i in range (1, 220):    X_train.append(sc_training_set[i-1:i, 0])    y_train.append(sc_training_set[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))from keras.models import Sequentialfrom keras.layers import Densefrom keras.layers import LSTMfrom keras.layers import Dropoutregressor = Sequential()regressor.add(LSTM(units = 64, return_sequences = True, input_shape = (X_train.shape[1], 1)))regressor.add(Dropout(0.2))regressor.add(LSTM(units = 128, return_sequences = True))regressor.add(Dropout(0.2))regressor.add(LSTM(units = 256, return_sequences = True))regressor.add(Dropout(0.2))regressor.add(LSTM(units = 512, return_sequences = True))regressor.add(Dropout(0.2))regressor.add(LSTM(units = 256, return_sequences = True))regressor.add(Dropout(0.2))regressor.add(LSTM(units = 128, return_sequences = True))regressor.add(Dropout(0.2))regressor.add(LSTM(units = 64))regressor.add(Dropout(0.2))regressor.add(Dense(units = 1))regressor.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])regressor.fit(X_train, y_train, epochs = 10, batch_size = 32)dataset_test = []X_test = []for i in range(220, 500):    X_test.append(sc_training_set[i-1:i, 0])X_test = np.array(X_test)X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))pred_stock = regressor.predict(X_test)pred_stock = sc.inverse_transform(pred_stock)

回答：

这里是预测未来的伪代码。基本上，您需要不断将最新的预测值添加到时间序列中。

您不能仅仅增加时间步长的大小，否则您将试图访问超出范围的索引。

predictions = []last_x = (the last x value in your data)while len(predictions) < #_of_predictions_you_want:    p = model.predict(last_x)    predictions.append(p)    last_x = np.roll(x, -1)    last_x[-1] = p