Hello,

I was wondering how the model predict a new data after train data then test data finally plot it. (Which is in my opinion that process is curve fitting). If you know Ms Excel in chart section there is chart and curve fitting and then forecaat it)

# 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)+(look_back*2)+1:len(dataset)-1, :] = testPredict

# plot baseline and predictions

plt.plot(scaler.inverse_transform(dataset))

plt.plot(trainPredictPlot)

plt.plot(testPredictPlot)

plt.show()

Letâ€™s say the total data is 100, it splits into 80 for training and 20 for testing

It has done well in plotting

The question is how to get the forecast for data number 101 (single forecast) or multi forecast.

Many thank for the help