Long Short-Term Memory network is a type of Recurrent Neural Network
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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
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# fix random seed for reproducibility
numpy.random.seed(7)
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# load the dataset
dataframe = pandas.read_csv('tweetvolumes.csv', usecols=[1], engine='python')
dataset = dataframe.values
dataset = dataset.astype('float32')
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dataframe.describe()
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# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
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# split into train and test sets - train set ends on May 12
train_size = 61
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size-10:len(dataset),:]
print(len(train), len(test))
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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)
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# reshape into X=t and Y=t+1
look_back = 7
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
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# 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]))
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# create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_dim=look_back))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, nb_epoch=100, batch_size=1, verbose=2)
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# 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))
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testPredict
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# 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-10)+(look_back)+1:len(dataset)-1, :] = testPredict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()
lookback = 1, hidden neurons = 4 Train Score: 398.36 RMSE Test Score: 314.96 RMSE
lookback = 3, hidden neurons = 4 Train Score: 387.69 RMSE Test Score: 214.87 RMSE
lookback = 7, hidden neurons = 4 Train Score: 357.99 RMSE Test Score: 146.85 RMSE
lookback = 10, hidden neurons = 8 Train Score: 366.99 RMSE Test Score: 149.24 RMSE
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