How to realize the Prediction of LSTM time Series by Python

This article mainly explains "how to achieve LSTM time series prediction by Python". Interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn "how to achieve LSTM time series prediction by Python".

Reference data:

There are two columns of data, the date on the left and the number of passengers on the right

Visualize the data:

Import mathimport 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# changes the integer to floatdataset = dataset.astype (' float32') plt.plot (dataset) plt.show ()

Visualization results:

Let's start with modeling:

Complete code:

Import mathimport 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.valuesdataset = dataset.astype (' float32') scaler = MinMaxScaler (feature_range = (0prime1)) dataset = scaler.fit_transform (dataset) train_size = int (len (dataset) * 0.67) test_size = len (dataset)-train_sizetrain,test = dataset [0: train_size,:], dataset [train _ size:len (dataset),:] look_back = 1trainXMagazine Y = 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 (4m inputbacks shape = (1m lookback)) model.add (Dense (1)) model.compile (loss = 'mean_squared_error') Optimizer = 'adam') model.fit (trainX,trainY,epochs = 100 recordable batchboxes = 1) # make predictionstrainPredict = model.predict (trainX) testPredict = model.predict (testX) # invert predictionstrainPredict = scaler.inverse_transform (trainPredict) trainY = scaler.inverse_transform ([trainY]) testPredict = scaler.inverse_transform (testPredict) testY = scaler.inverse_transform ([testY]) # calculate root mean squared errortrainScore = 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 plottingtrainPredictPlot = numpy.empty_like (dataset) trainPredictPlot [:,:] = numpy.nanometric Predictive PlotPlotPlotPlotPlotPlotlook _ back:len (trainPredict) + look_back,:] = trainPredict# shift test predictions for plottingtestPredictPlot = numpy.empty_like (dataset) testPredictPlot [: :] = numpy.nantestPredictPlot [len (trainPredict) + (look_back*2) + 1:len (dataset)-1,:] = testPredict# plot baseline and predictionsplt.plot (scaler.inverse_transform (dataset)) plt.plot (trainPredictPlot) plt.plot (testPredictPlot) plt.show ()

Running result:

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