I am trying to predict some time series based on precedent values using LSTM.
I have pretty good results when I compare the predicted time series with the test set (0,18% error)
I just miss how to forecast outside the interval of data ^^'
I have to admit that I used a point by point prediction method that looks like this:
def predict_point_by_point(model, data): predicted = model.predict(data) predicted = np.reshape(predicted, (predicted.size)) return predicted
I then, I used it to override the
maybe the original function could have nailed the prediction to have a future time series? maybe the point by point isn't that bad neither?
I mean; how could I predict, some precise interval of time series (3months for example) without just reffering to the test set?
Example: the test set starts 01/01/2018 and ends 01/12/2018 and I want to predict 4 months from 02/12/2018
Thanks in advance for your help
model.predict([x_test]), wich will give me a time series in the same time interval of x_test, I use an other approch to have a future time series Thanks a lot $\endgroup$