Python深度学习教程：LSTM时间序列预测小练习—国航乘客数量预测
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：importmath
importnumpyasnp
importpandasaspd
importmatplotlib.pyplotasplt
frompandasimportread_csv
fromkeras.modelsimportSequential
fromkeras.layersimportDense
fromkeras.layersimportLSTM
fromsklearn.preprocessingimportMinMaxScaler
fromsklearn.metricsimportmean_squared_error
#loaddataset
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：
完整代码：importmath
importnumpy
importpandasaspd
importmatplotlib.pyplotasplt
frompandasimportread_csv
fromkeras.modelsimportSequential
fromkeras.layersimportDense
fromkeras.layersimportLSTM
fromsklearn.preprocessingimportMinMaxScaler
fromsklearn.metricsimportmean_squared_error
defcreate_dataset(dataset,look_back=1):
dataX,dataY=[],[]
foriinrange(len(dataset)-look_back-1):
a=dataset[i:i+look_back,0]
b=dataset[i+look_back,0]
dataX.append(a)
dataY.append(b)
returnnumpy.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)
#reshapeinputtobe[samples,timesteps,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]))
#createandfittheLSTMnetwork
model=Sequential()
model.add(LSTM(4,input_shape=(1,look_back)))
model.add(Dense(1))
pile(loss='mean_squared_error',optimizer='adam')
model.fit(trainX,trainY,epochs=100,batch_size=1,verbose=2)
#makepredictions
trainPredict=model.predict(trainX)
testPredict=model.predict(testX)
#invertpredictions
trainPredict=scaler.inverse_transform(trainPredict)
trainY=scaler.inverse_transform([trainY])
testPredict=scaler.inverse_transform(testPredict)
testY=scaler.inverse_transform([testY])
#calculaterootmeansquarederror
trainScore=math.sqrt(mean_squared_error(trainY[0],trainPredict[:,0]))
print('TrainScore:%.2fRMSE'%(trainScore))
testScore=math.sqrt(mean_squared_error(testY[0],testPredict[:,0]))
print('TestScore:%.2fRMSE'%(testScore))
#shifttrainpredictionsforplotting
trainPredictPlot=numpy.empty_like(dataset)
trainPredictPlot[:,:]=numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back,:]=trainPredict
#shifttestpredictionsforplotting
testPredictPlot=numpy.empty_like(dataset)
testPredictPlot[:,:]=numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1,:]=testPredict
#plotbaselineandpredictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()
运行结果：
本次的Python学习教程！
python时间序列分析航空旅人_Python深度学习教程：LSTM时间序列预测小练习—国航乘客数量预测...
