#StackBounty: #time-series #neural-networks #forecasting #predictive-models #keras what does it mean when keras+tensorflow predicts all…

Bounty: 100

From what I understand is that in supervised learning problems there is a dependent variable Y, which I included in my ANN. There is one set of matching predictions for each sample for each Y. The number of predictions should match the number of true values given.

The problem I’m having is that after using model.predict() in Keras the ANN is giving me the Y dependent variable + the 10 timesteps of the Y variable that I gave for the predictors (i think).

My training dataset includes 10 timesteps for each variable. I assumed that I could use timesteps to insert lagged versions of each predictor variable.

Basically I don’t understand what these 10 predicted timesteps for the Y variable are. They are not lagged versions of the predicted Y at time t.

The reason I’m asking is that I don’t know if the global score of the model should really include predicted timesteps of Y. Should I ignore it or include them?

Also, in terms of prediction which values do I use? Just the ones at time T?

Is the Y(t-1) the predicted values for all the predictors at timestep (t-1) like Y?


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