如何使用CNN-LSTM提前10天预测。我使用了WTI数据

Question

我正在使用美国网站的WTI数据。我正在使用CNN-LSTM模型进行预测和预测。我的问题是我不了解如何提前10天预测。我已经运行了所有这些程序，没有任何错误。我在Python中使用Jupyter笔记本。预测和图形成功运行。

我的程序：

import numpy
import pandas

import math
import matplotlib.pyplot as plt
from numpy import array
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import TimeDistributed
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_percentage_error
# load the dataset
dataframe = pandas.read_csv('C:/Users/HP/Desktop/python/WTI.csv', usecols=[1], engine='python')
dataset = dataframe.values
dataset = dataset.astype('float32')
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# split into train and test sets
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
print(len(train), len(test))
# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return numpy.array(dataX), numpy.array(dataY)
# reshape into X=t and Y=t+1
look_back = 1
# choose a number of time steps
n_steps = 2
# split into samples

# reshape from [samples, timesteps] into [samples, subsequences, timesteps, features]


trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
n_features = 1
n_seq = 1
n_steps = 1

# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], n_seq, n_steps, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], n_seq, n_steps, testX.shape[1]))
# define model
model = Sequential()
model.add(TimeDistributed(Conv1D(filters=64, kernel_size=1, activation='relu'), input_shape=(None,1, look_back)))
model.add(TimeDistributed(Flatten()))
model.add(LSTM(50, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
model.fit(trainX, trainY, epochs=20, batch_size=1, verbose=2)
# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# invert predictions
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
MAE = mean_absolute_error(trainY[0], trainPredict[:,0])
print(MAE)
trainMAE = mean_absolute_error(trainY[0], trainPredict[:,0])
print(trainMAE)
testMAE = mean_absolute_error(testY[0], testPredict[:,0])
print(testMAE)
trainmape = mean_absolute_percentage_error(trainY[0], trainPredict[:,0])
print(trainmape)
testmape= mean_absolute_percentage_error(testY[0],testPredict[:,0])
print(testmape)
# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()