参考数据：
数据一共两列，左边是日期，右边是乘客数量

对数据做可视化：

import math
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
from pandas import read_csv 
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 

#load dataset 
dataframe = read_csv('./international-airline-passengers.csv',usecols =[1],header = None,engine = 'python',skipfooter = 3)
dataset   = dataframe.values
#将整型变为float
dataset = dataset.astype('float32')
plt.plot(dataset)
plt.show() 

可视化结果：

下面开始进行建模：
具体细节参考：

https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/

完整代码：

import math
import numpy  
import pandas as pd 
import matplotlib.pyplot as plt 
from pandas import read_csv 
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 

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]
		b = dataset[i+look_back,0]
		dataX.append(a)
		dataY.append(b)
	return numpy.array(dataX),numpy.array(dataY)

numpy.random.seed(7)

dataframe = read_csv('./international-airline-passengers.csv',usecols = [1],header = None,engine = 'python')
dataset   = dataframe.values
dataset   = dataset.astype('float32')

scaler = MinMaxScaler(feature_range = (0,1))
dataset = scaler.fit_transform(dataset)

train_size = int(len(dataset) * 0.67)
test_size  = len(dataset) - train_size
train,test = dataset[0:train_size,:],dataset[train_size:len(dataset),:]

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],look_back,trainX.shape[1]))
testX  = numpy.reshape(testX,(testX.shape[0],look_back,testX.shape[1]))

#create and fit the LSTM network 
model = Sequential()
model.add(LSTM(4,input_shape = (1,look_back)))
model.add(Dense(1))
model.compile(loss = 'mean_squared_error',optimizer = 'adam')
model.fit(trainX,trainY,epochs = 100,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))
# 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()

运行结果：

参考教程：

https://www.cnblogs.com/sakuraXiYue/p/10327760.html
https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/