MATLAB: Time Series forecasting with 3 input data NARnet or NARXnet

Deep Learning Toolboxforecastingnarnetnarxnetneural networktimeseries

My Input Data is given in this form in MS-Excel spreadsheet:
Date               OilRate(BOPD) GasProduced(MscfD)                   WaterRate(BOPD)
7/27/2008          4108                4620                             2.08
7/28/2008          4557               5120                             5.54
7/29/2008          4128                4640                             5.03
7/30/2008          5722                6730                             9.25
7/31/2008          3321                3720                             7.11
For up to 2000 timesteps over 3 years;
My questions are:
  1. I would like to predict future values of the above 3 for up to 2000 timesteps. (giving me a 3 input, 3 output)?
  2. Do I import the data as a matrix or a cell array?
  3. Would you recommend a NARnet or NARXnet, I have used NARNET with the ntstool
  4. I tried adopting the code in this tutorial: http://www.mathworks.com/matlabcentral/newsreader/view_thread/338508#934732but plotting was giving me a problem as the sample data set was a 1×100 cell and my data is 3×1500 (utilized only 1500 timesteps)
Any useful suggestions are highly appreciated.
Thanks!

Best Answer

Mathematically, you have the choice of choosing any combination of the 3 series to be inputs and any, possibly other combination, to be outputs.
Physically, are any considered inputs and others considered outputs?
It might be worthwhile to use nncorr or fft to obtain the 3 autocorrelation functions and the 3 crosscorrelation functions. Then you can see which lags are the most important for prediction.
Use cells for series but convert to doubles for plots.
Greg
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