使用LSTM对时间序列数据进行多步预测

Question

我有549个数据点（它是每日数据），我想预测未来60天。我对测试数据集有一定的下降预测，但是我很难尽早进行预测。

import numpy
import matplotlib.pyplot as plt
import pandas
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error


scaler = MinMaxScaler(feature_range=(-1, 1))
dataset = scaler.fit_transform(daily_data)

train, test = dataset[:365], dataset[365: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)


from keras.layers import Dropout

# reshape into X=t and Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)


# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0],1,trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))


# create and fit the LSTM network
model = Sequential()
model.add(LSTM(50, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=150, 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))



 from pylab import rcParams
 rcParams['figure.figsize'] = 18, 5


 # 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()

我希望能够比预测提前60个时间步。 LSTM的新手，并引用了以下文章：

https://machinelearningmastery.com/how-to-develop-lstm-models-for-multi-step-time-series-forecasting-of-household-power-consumption/

有人可以帮忙吗？