Vfrdtyky

This is my first time with Keras and LSTMs and I am working in a project in which I have many time series data to train with.

I have around 13000 rows of data (1 column) with numerical values regarding to a degradation level of a component ending in a failure; and on the other side I have multiple datasets of 100 rows (1 column) with data regarding to a degradation level of a component, but ending some points before a failure.

The challenge is to predict when those datasets will record a failure.

So what I have done, is the next:

from pandas import DataFrame

from pandas import Series

from pandas import concat

from pandas import read_csv

from pandas import datetime

from sklearn.metrics import mean_squared_error

from sklearn.preprocessing import MinMaxScaler

from keras.models import Sequential

from keras.layers import Dense

from keras.layers import LSTM

from math import sqrt

from matplotlib import pyplot

from numpy import array





# convert an array of values into a dataset matrix

def create_dataset(dataset, look_back):

    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)





look_back = 600

epochs = 500

batch_size = 50









data = np.array(data).reshape(-1,1)

data = data.astype('float32')



# Scale the data

scaler = MinMaxScaler(feature_range=(0, 1))

dataset = scaler.fit_transform(data)



# split into train and test sets

train_size = int(len(dataset) * 0.67)

test_size = len(dataset) - train_size

train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]



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(100, activation = 'tanh', inner_activation = 'hard_sigmoid', return_sequences=True))

model.add(LSTM(50, activation = 'tanh', inner_activation = 'hard_sigmoid'))

model.add(Dense(1))

model.compile(loss='mean_squared_error', optimizer='adam')

model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, 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))

These line codes represents the training and the evaluation of the dataset, but how can I predict the next (for example) 50 elements of one dataset of 100 rows using that model?

Try this:
model.predict(newX)