I have a data csv file including with three inputs and two output with time series. Here data took an every one hour one hour. So I need to predict my next future value at t+60 according to the previous input value and at that time period if having new input value using regression neural network. So I choose LSTM neural network to predict next future value. But I don't know how to give time period to predict my future value. Can anyone suggest me how to solve this problem? Can anyone give me any examples to clear out this problem? Here that prediction value will come as input value (g).

subset of my csv file

enter image description here

here I upload my code;

def create_data(data, look_back=1):
dataX, dataY = [], []
for i in range(len(data) - look_back - 1):
    a = data[i:(i + look_back), :]
    dataY.append(data[i + look_back, 2])
return numpy.array(dataX), numpy.array(dataY)

data = pd.DataFrame(data,columns=['g','p','c'])
data = data.values
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)
train_size = int(len(data) * 0.67) 
test_size = len(data) - train_size
train, test = data[0:train_size, :], data[train_size:len(data), :]
# reshape into X=t and Y=t+1
look_back = 3
trainX, trainY = create_data(train, look_back)  
testX, testY = create_data(test, look_back)
# reshape input to be  [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], look_back, 3))
testX = numpy.reshape(testX, (testX.shape[0],look_back, 3))
model = Sequential()
model.add(LSTM(6, input_shape=(look_back,3)))
model.compile(loss='mean_squared_error', optimizer='adam')
history= model.fit(trainX, trainY,validation_split=0.33, nb_epoch=10, 
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
trainPredict_extended = numpy.zeros((len(trainPredict),3))
trainPredict_extended[:,2] = trainPredict[:,0]
trainPredict = scaler.inverse_transform(trainPredict_extended) [:,2]  
testPredict_extended = numpy.zeros((len(testPredict),3))
testPredict_extended[:,2] = testPredict[:,0]
testPredict = scaler.inverse_transform(testPredict_extended)[:,2]   
trainY_extended = numpy.zeros((len(trainY),3))
testY_extended = numpy.zeros((len(testY),3))
trainScore = math.sqrt(mean_squared_error(trainY, trainPredict))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY, testPredict))
print('Test Score: %.2f RMSE' % (testScore))
# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(data)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, 2] = trainPredict

# shift test predictions for plotting
testPredictPlot = numpy.empty_like(data)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(data)-1, 2] = 

1 Answer 1


One option is to :

  1. Break data into constant-frequency observations (E.g.: Assume that g,p,c,out are same for all time periods between two observations). With this, you will get samples every N minutes.
  2. Training data will then be a set of [Last M observations till T, Observation for T + 60]

This Train and test data can be fed into a network.

  • $\begingroup$ I'm really new to the LSTM python. I guess your idea is better, but I don't know how to apply in this to the code. Can you provide me an example for this with code? It is really helipful to me if you can. Thank you. $\endgroup$
    – user59020
    Jan 22, 2019 at 8:24
  • $\begingroup$ You can combine examples from these 2 articles : machinelearningmastery.com/… (Basic LSTM example) machinelearningmastery.com/… (LSTM with multiple variables) $\endgroup$ Jan 22, 2019 at 8:27
  • $\begingroup$ Thank you for providing me the examples. Here I upload my code, can you tell me what changes should I have to do? $\endgroup$
    – user59020
    Jan 22, 2019 at 8:47
  • $\begingroup$ What is the accuracy after training on this model (this has just 1 dense layer and very simple. You might have to increase complexity of model) $\endgroup$ Jan 22, 2019 at 9:43
  • $\begingroup$ accuracy is 0.5. If I want predict two output then what should I have to change? $\endgroup$
    – user59020
    Jan 22, 2019 at 10:18

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