I'm trying to implement a multi-variate, multiple-step model to forecast the day ahead electricity prices (h+1,h+2,...,h+24). I know how to do one forecast, but I'm confused with how to implement a multiple output approach where each forecast gives a vector of predictions for each hour in the day head for one shot (the multiple output strategy in https://machinelearningmastery.com/multi-step-time-series-forecasting/).
The gist of what I've done is get the code that predicts one time step, and modify it by converting the output into a sequence for the next 24 hours time shift, instead of just say one ElecPrice shifted 24 hours into the future:
Y['ElecPrice1'] = df['RT_LMP'].shift(-1) Y['ElecPrice2'] = df['RT_LMP'].shift(-2) Y['ElecPrice3'] = df['RT_LMP'].shift(-3) ... Y['ElecPrice3'] = df['RT_LMP'].shift(-24)
I also changed the number of output signals my model has to predict.
Basically I made an entire 24 new columns instead of just one for my predictions. I have adjusted my dataset accordingly (no dangling NaNs, etc.). It seems too simple that I'm worrying if all I'm doing is making a prediction for one hour and copying that for the rest of the hours.
The sample code can be viewed in this link https://nbviewer.jupyter.org/github/Joichiro666/Forecasting-sharing/blob/master/LSTM-DNN%203.ipynb (ignore anything below generate predictions)
I just want to confirm if what I'm doing is correct or not.